Temporal Climate Variability Research Articles

Forecasting Blue and Green Water Footprint of Wheat Based on Single, Hybrid, and Stacking Ensemble Machine Learning Algorithms Under Diverse Agro-Climatic Conditions in Nile Delta, Egypt

The aim of this research is to develop and compare single, hybrid, and stacking ensemble machine learning models under spatial and temporal climate variations in the Nile Delta regarding the estimation of the blue and green water footprint (BWFP and GWFP) for wheat. Thus, four single machine learning models (XGB, RF, LASSO, and CatBoost) and eight hybrid machine learning models (XGB-RF, XGB-LASSO, XGB-CatBoost, RF-LASSO, CatBoost-LASSO, CatBoost-RF, XGB-RF-LASSO, and XGB-CatBoost-LASSO) were used, along with stacking ensembles, with five scenarios including climate and crop parameters and remote sensing-based indices. The highest R2 value for predicting wheat BWFP was achieved with XGB-LASSO under scenario 4 at 100%, while the minimum was 0.16 with LASSO under scenario 3 (remote sensing indices). To predict wheat GWFP, the highest R2 value of 100% was achieved with RF-LASSO across scenario 1 (all parameters), scenario 2 (climate parameters), scenario 4 (Peeff, Tmax, Tmin, and SA), and scenario 5 (Peeff and Tmax). The lowest value was recorded with LASSO and scenario 3. The use of individual and hybrid machine learning models showed high efficiency in predicting the blue and green water footprint of wheat, with high ratings according to statistical performance standards. However, the hybrid programs, whether binary or triple, outperformed both the single models and stacking ensemble.

Climate data integration into wheat performance evaluation reveals large inter-varietal responses

Abstract There is an urgent need to adapt crop breeding strategies to boost resilience in the face of a growing food demand and a changing climate. Achieving this requires an understanding of how weather and climate variability impacts crop growth and development. Using the United Kingdom (UK) as an example, we evaluate changes in the UK agroclimate and analyse how these have influenced domestic wheat production. Here we quantify spatial and temporal variability and changes in weather and climate across growing seasons over the last four decades (1981-2020). Drawing on variety trial data, we then use statistical modelling to explore the interaction between genotype and agroclimate variation. 
We show that changes in the UK agroclimate present both risks, and opportunities for wheat growers, depending on location. From 1981-2020, in Wales, the West Midlands, large parts of the North West, and Northern Ireland, there was an overall increase in frost risk in early spring of 0.15 additional frost days per year, whilst in the East early frost risk decreased by up to 0.29 days per year. Meanwhile, over the period 1987-2020, surface incoming shortwave radiation during grainfill increased in the East by up to 13% but decreased in Western areas by up to 15%. We show significant inter-varietal differences in yield responses to growing degree days, heavy rainfall and the occurrence of late frost. This highlights the importance of evaluating variety-climate interactions in variety trial analyses, and in climate-optimised selection of crops and varieties by growers. This work provides guidance for future research on how climate change is affecting the UK agroclimate and resulting impacts on winter cereal production.

"Two zones and three centers" distribution and suitable areas shift of an evergreen oak in subtropical China under climate scenarios.

Understanding the impact of climate change on the geographical distribution of species is a fundamental requirement for biodiversity conservation and resource management. Quercus oxyphylla, an evergreen oak endemic to China, plays a crucial role in maintaining the ecological stability in subtropical regions and high economic value attributed to its dark and high-density heartwood, but the existing resources are close to endangered. Currently, limited knowledge exists regarding its distribution and potential influences of climate change on suitable areas. This study utilized 63 occurrence records and Biomod2 platform, to predict changes in suitable areas for Q. oxyphylla under future climate change. The results revealed that (1) Q. oxyphylla showed a pattern of three disjunctive geographical centers in the eastern subregion of subtropical evergreen broad-leaved forest region (IVA): Qinling-Daba Mountains, Nanling Mountains and Wuyi Mountains center. Currently, the highly suitable areas concentrated in two zones divided by the Yangtze River, that is, the northern subtropical evergreen and deciduous broad-leaved forest zone (IVAii) and the mid-subtropical evergreen broad-leaved forest zone (IVAi). (2) The temperature-related variables, such as annual temperature range (Bio7), the mean diurnal range (Bio2), and annual mean temperature (Bio1), were identified as the key determinants of the distribution pattern. Because of its considerable climatic variations in temperature and water conditions, Q. oxyphylla's habitat displayed a wider climate niche and strong physiological tolerance to climate change. (3) Under future climate scenarios, the suitable area of the species was expected to overall expand with significant regional differences. The suitable area in IVAi was expected to expand significantly northward while that in IVAii was expected to gradually shrink. To address the impact of climate change, it is necessary to develop conservation plans focused around the three distribution centers, implement localized and regional conservation policies, and conduct educational outreach among local people.

Assessing teleconnection influences on the spatial and temporal patterns of meteorological drought in Northwest China

ABSTRACT This study delves into the complex relationship between teleconnection patterns and meteorological drought in Northwest China, highlighting the crucial influence of global circulation indices on regional drought dynamics. By analyzing precipitation and temperature data from 1962 to 2022 using the CN05.1 datasets and incorporating various global circulation indices, the study employs the Standardized Precipitation Evapotranspiration Index (SPEI) to delineate drought conditions. Utilizing the Modified Mann-Kendall test and segmented models for trend and change point detection, along with cross wavelet transforms (XWT) and wavelet coherence (WTC) analysis to examine the impact of 15 global circulation indices, the study uncovers significant spatial and temporal climatic variations. Findings indicate a significant increase in temperature and precipitation, with March-April-May (MAM) season showing pronounced drought severity mainly due to a significant temperature rise with a value of 0.0420 °C/year. Change point analysis reveals pivotal shifts in climate, highlighting the region’s susceptibility to climate change. The study identified strong correlations between drought occurrences and global circulation indices like the Artic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO) and the Southern Oscillation Index (SOI), especially within a 4–8 year timeframe, pointing to the significant role of teleconnections in affecting local drought conditions. These insights are vital for formulating effective drought management and climate adaptation strategies in arid and semi-arid regions, offering valuable guidance for policymakers and researchers focused on improving water resource management and enhancing climate resilience in such vulnerable environments.

Contrasting responses of forest phenological guilds to complex floodplain change

Abstract Understanding the seasonal dynamics of the forest herb layer and the factors that influence it is essential for predicting how the forest ecosystem, including mutualistic interactions, will respond to global change. However, to date, no research has investigated how understorey phenological guilds respond to major environmental threats and forest interventions. We showed the marked changes in the phenological structure of floodplain forest plant communities over half a century at a multi‐regional scale in Central Europe. Unlike previous studies focusing on individual species' responses to climate change, we examined shifts in species richness, frequency and abundance between phenological guilds within the plant community. We examined the effects of temporal variation in climate, hydrology, soil conditions and canopy structure, and assessed the effects of non‐intervention and intervention management on phenological guild responses. Our results show a significant loss of local species richness and a decline in species frequency in the forest understorey vegetation, associated with species exhibiting summer phenology. In contrast, an increase in abundance and richness of spring flowering species was observed, attributed to high nutrient loads and reduced flooding, respectively. The abundance of spring species increased only in stands where canopy cover increased over time, probably due to the suppression of summer flowering species, allowing spring flowering species to spread on open, bare ground. The community experienced a shift in maximum flowering plant richness from June to May. Despite management interventions such as clearcutting and tree planting, our data show similar temporal trajectories in intervention and non‐intervention forests, indicating that changes in phenological structure are largely independent of recent management activities. Synthesis. We propose that a complex interplay of environmental factors, rather than climate change alone, is shaping shifts towards earlier phenological guilds in floodplain forests. This study highlights the importance of considering a comprehensive view of phenological guild dynamics and the impact of environmental factors on forest plant community structure in the face of global change.

High-resolution Annual Dynamic dataset of Curve Number from 2008 to 2021 over Conterminous United States

The spatial distribution and data quality of curve number (CN) values determine the performance of hydrological estimations. However, existing CN datasets are constrained by universal-applicability hypothesis, medium resolution, and imbalance between specificity CN tables to generalized land use/land cover (LULC) maps, which hinder their applicability and predictive accuracy. A new annual CN dataset named CUSCN30, featuring an enhanced resolution of 30 meters and accounting for temporal variations in climate and LULC in the continental United States (CONUS) between 2008 and 2021, was developed in this study. CUSCN30 demonstrated good performance in surface runoff estimation using CN method when compared to observed surface runoff for the selected watersheds. Compared with existing CN datasets, CUSCN30 exhibits the highest accuracy in runoff estimation for both normal and extreme rainfall events. In addition, CUSCN30, with its high spatial resolution, better captures the spatial heterogeneity of watersheds. This developed CN dataset can be used as input for hydrological models or machine learning algorithms to simulate rainfall-runoff across multiple spatiotemporal scales.

Temporal variability in irrigated land and climate influences on salinity loading across the Upper Colorado River Basin, 1986-2017

Freshwater salinization is a growing global concern impacting human and ecosystem needs with impacts to water availability for human and ecological uses. In the Upper Colorado River Basin (UCRB), dissolved solids in streams compound ongoing water supply challenges to further limit water availability and cause economic damages. Much effort has been dedicated to understanding dissolved solid sources, transport, and management in the region, yet temporal variability in loading from key sources such as irrigated lands and the influence of climate on dissolved solids loading remains unknown. Quantifying the contributions and temporal variability of dissolved solids loads from irrigated lands may benefit salinity management efforts. This study applies a time-varying (dynamic) modeling approach to predict annual dissolved solids loads across the UCRB from 1986 through 2017. Between 66% and 82% of the total accumulated dissolved solids load in the basin is from groundwater (storage and baseflow). Our findings link climate, irrigation, and groundwater, and confirm large storage contributions that have declined slightly with time. Dissolved solids loads increase during wet periods and decrease during dry periods, although the relative contributions of different sources vary little with time. Irrigation enhances loading efficiency relative to unirrigated areas through runoff and groundwater, and can locally be a major source of dissolved solids where irrigation occurs. Results indicate that loads from irrigated areas increase when irrigated area and/or water available for runoff increase. Increased regional aridification over the study period may have contributed to decreasing stream salinity through both quicker surface runoff and lagged groundwater storage processes. Study results may be relevant to salinity management in arid environments where water availability is limited and where irrigation influences salinity loading to streams.

A species' response to spatial climatic variation does not predict its response to climate change.

The dominant paradigm for assessing ecological responses to climate change assumes that future states of individuals and populations can be predicted by current, species-wide performance variation across spatial climatic gradients. However, if the fates of ecological systems are better predicted by past responses to in situ climatic variation through time, this current analytical paradigm may be severely misleading. Empirically testing whether spatial or temporal climate responses better predict how species respond to climate change has been elusive, largely due to restrictive data requirements. Here, we leverage a newly collected network of ponderosa pine tree-ring time series to test whether statistically inferred responses to spatial versus temporal climatic variation better predict how trees have responded to recent climate change. When compared to observed tree growth responses to climate change since 1980, predictions derived from spatial climatic variation were wrong in both magnitude and direction. This was not the case for predictions derived from climatic variation through time, which were able to replicate observed responses well. Future climate scenarios through the end of the 21st century exacerbated these disparities. These results suggest that the currently dominant paradigm of forecasting the ecological impacts of climate change based on spatial climatic variation may be severely misleading over decadal to centennial timescales.

Groundwater Sensitivity to Climate Variations Across Australia

AbstractGroundwater response to climate variations is often pivotal to managing groundwater sustainably. However, this relationship is rarely explicitly examined because of the complexity of surface to subsurface processes and the diverse impacts of multiple drivers, such as groundwater pumping and land use changes. In this paper, we address this challenge by proposing methods to quantify the sensitivity of groundwater level and recharge to temporal climate variability across Australia. Using the HydroSight groundwater hydrograph toolbox we first identify 1,143 out of a total of 4,350 bores as climate‐driven, where historically, head was primarily driven by climate variations. Streamflow elasticity measures are then adapted to groundwater to quantify the long‐term head and recharge sensitivity. We find that the national median sensitivity of head and recharge to precipitation change are 42 and 0.43 mm mm−1, respectively (interquartiles: 20–77 and 0.30–0.55 mm mm−1); both of which are ∼8 times that of potential evapotranspiration. Nationally, the results are spatially correlated, suggestive of large‐scale effects. The responses of head and recharge appear to be primarily related to climate type and hydrogeology. The more arid the climate, the higher the head sensitivity but the lower the recharge sensitivity. Porous media generally show higher head sensitivity than fractured media due to smaller aquifer specific yield, and again contrarily for that of recharge. These findings contribute to understanding the long‐term impact of climate change on groundwater and thus provide valuable insights for sustainable groundwater management.

Study of spatial and temporal climate variability in the Siguiri sub-basin (North-Eastern Guinea)

Study of spatial and temporal climate variability in the Siguiri sub-basin (North-Eastern Guinea)

Macroecological predictors of evolutionary and plastic potential do not apply at microgeographic scales for a freshwater cladoceran under climate change.

Rapid evolutionary adaptation could reduce the negative impacts of climate change if sufficient heritability of key traits exists under future climate conditions. Plastic responses to climate change could also reduce negative impacts. Understanding which populations are likely to respond via evolution or plasticity could therefore improve estimates of extinction risk. A large body of research suggests that the evolutionary and plastic potential of a population can be predicted by the degree of spatial and temporal climatic variation it experiences. However, we know little about the scale at which these relationships apply. Here, we test if spatial and temporal variation in temperature affects genetic variation and plasticity of fitness and a key thermal tolerance trait (critical thermal maximum; CTmax) at microgeographic scales using a metapopulation of Daphnia magna in freshwater rock pools. Specifically, we ask if (a) there is a microgeographic adaptation of CTmax and fitness to differences in temperature among the pools, (b) pools with greater temporal temperature variation have more genetic variation or plasticity in CTmax or fitness, and (c) increases in temperature affect the heritability of CTmax and fitness. Although we observed genetic variation and plasticity in CTmax and fitness, and differences in fitness among pools, we did not find support for the predicted relationships between temperature variation and genetic variation or plasticity. Furthermore, the genetic variation and plasticity we observed in CTmax are unlikely sufficient to reduce the impacts of climate change. CTmax plasticity was minimal and heritability was 72% lower when D. magna developed at the higher temperatures predicted under climate change. In contrast, the heritability of fitness increased by 53% under warmer temperatures, suggesting an increase in overall evolutionary potential unrelated to CTmax under climate change. More research is needed to understand the evolutionary and plastic potential under climate change and how that potential will be altered in future climates.

Spatiotemporal climate and vegetation trends, and their relationship: A case of Genale Dawa basin, Ethiopia

Understanding the variability of climate and vegetation, and their relationship in space and time are crucial for agricultural planning, flood risk assessment, and drought monitoring. However, most studies depend on scarce and unevenly distributed climate station datasets in most basins of Ethiopia like the Genale Dawa basin. Detailed climate and vegetation variabilities and their relationship have not well studied in the Genale Dawa basin, which is often affected by recurrent drought. Satellite remote sensing-derived high spatial resolution climate and vegetation data can provide detailed information on climate variability and vegetation changes. This study assessed spatiotemporal variability of temperature, rainfall, and vegetation greenness and their relationship in the Genale Dawa basin. For this study, temperature (minimum and maximum), rainfall data (4 × 4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data (250 × 250 m) were used. Statistical indices like the Mann Kendall (MK) trend test, Coefficient of Variation (CV), Standard Anomaly Index (SAI), Precipitations Concentration Index (PCI), and Pearson correlation techniques were used to examine the trends and variabilities. The result generally indicated increasing trends in rainfall during the dry (“Bega”) season, and maximum and minimum temperatures during Kiremt (Wet season) and annual seasons. Moreover, high rainfall variability in the Dry and Kiremt seasons and low variability of temperature at all timescales were observed in the basin. In addition, anomalies in temperatures (minimum and maximum) and rainfall were observed in the basin during all the timescales. The result further showed a strong correlation of NDVI with rainfall at annual (r = 0.58), dry season (r = 0.9), and the short rainy season (r = 0.7) while weakly correlated at Kiremt (r = 0.31) seasons in the Genale Dawa basin. The result generally showed spatial and temporal variability of climate and vegetation greenness in the Genale Dawa basin, which negatively affects agricultural production, water resource availability, and vegetation growth. Therefore, appropriate management strategies should be implemented to minimize the negative effects.

Effects of changing climate extremes and vegetation phenology on wildlife associated with grasslands in the southwestern United States

Assessments of the potential responses of animal species to climate change often rely on correlations between long-term average temperature or precipitation and species’ occurrence or abundance. Such assessments do not account for the potential predictive capacity of either climate extremes and variability or the indirect effects of climate as mediated by plant phenology. By contrast, we projected responses of wildlife in desert grasslands of the southwestern United States to future climate means, extremes, and variability and changes in the timing and magnitude of primary productivity. We used historical climate data and remotely sensed phenology metrics to develop predictive models of climate-phenology relations and to project phenology given anticipated future climate. We used wildlife survey data to develop models of wildlife-climate and wildlife-phenology relations. Then, on the basis of the modeled relations between climate and phenology variables, and expectations of future climate change, we projected the occurrence or density of four species of management interest associated with these grasslands: Gambel’s Quail (Callipepla gambelii), Scaled Quail (Callipepla squamat), Gunnison’s prairie dog (Cynomys gunnisoni), and American pronghorn (Antilocapra americana). Our results illustrated that climate extremes and plant phenology may contribute more to projecting wildlife responses to climate change than climate means. Monthly climate extremes and phenology variables were influential predictors of population measures of all four species. For three species, models that included climate extremes as predictors outperformed models that did not include extremes. The most important predictors, and months in which the predictors were most relevant to wildlife occurrence or density, varied among species. Our results highlighted that spatial and temporal variability in climate, phenology, and population measures may limit the utility of climate averages-based bioclimatic niche models for informing wildlife management actions, and may suggest priorities for sustained data collection and continued analysis.

Exploring site-specific N application rate to reduce N footprint and increase crop production for green manure-rice rotation system in southern China

Milk vetch (Astragalus sinicus L.) is leguminous green manure (GM) which produces organic nitrogen (N) for subsequent crops and is widely planted and utilized to simultaneously reduce the use of synthetic N fertilizer and its environmental costs in rice systems. Determination of an optimal N application rate specific to the GM-rice system is challenging because of the large temporal and spatial variations in soil, climate, and field management conditions. To solve this problem, we developed a framework to explore the site-specific N application rate for the low-N footprint rice production system in southern China based on multi-site field experiments, farmer field survey, and process-based model (WHCNS_Rice, soil water heat carbon nitrogen simulator for rice). The results showed that a process-based model can explain >83.3% (p < 0.01) of the variation in rice yield, aboveground biomass, crop N uptake, and soil mineral N. Based on the scenario analysis of the tested WHCNS_Rice model, the simple regression equation was developed to implement site-specific N application rates that considered variations in GM biomass, soil, and climatic conditions. Simulation evaluation on nine provinces in southern China showed that the site-specific N application rate reduced regional synthetic N fertilizer input by 29.6 ± 17.8% and 65.3 ± 23.0% for single and early rice, respectively; decreased their total N footprints (NFs) by 23.4% and 49.3%, respectively; and without reduction in rice yield, compared with traditional farming N practices. The reduction in total NF was attributed to the reduced emissions from ammonia volatilization by 35.2%, N leaching by 28.4%, and N runoff by 32.7%. In this study, we suggested a low NF rice production system that can be obtained by combining GM with site-specific N application rate in southern China.

Spatially coherent variability in modern orographic precipitation produces asymmetric paleo-glacier extents in flowline models: Olympic Mountains, USA

Abstract. Glaciers are sensitive to temporal climate variability. Glacier sensitivity to spatial variability in climate has been much less studied. The Olympic Mountains of Washington state, USA, experience a pronounced orographic precipitation gradient, with modern annual precipitation ranging between ∼6500 and ∼500 mm water equivalent. In the Quinault valley, on the wet side of the range, a glacier extended onto the coastal plain, reaching a maximum position during the Early Wisconsin glaciation. On the dry side of the range, in the Elwha valley, there is no evidence of a large paleo-glacier during the Wisconsin glaciation. We hypothesize that asymmetry in the past glacier extent was driven by spatial variability in precipitation. To evaluate this hypothesis, we constrain the past precipitation gradient and model the glacier extent. We explore variability in observed and modeled precipitation gradients over timescales from 6 h to ∼100 yr. Across three datasets, basin-averaged precipitation in the Elwha is 54 % of that in the Quinault. Our analysis overwhelmingly indicates spatially coherent variability in precipitation across the peninsula. We conclude that the past precipitation gradient was likely similar to the modern gradient. We use a one-dimensional glacier flowline model, driven by sea level summer temperature and annual precipitation to approximate the glacier extent in the Quinault and Elwha valleys. We find several equilibrium states for the Quinault glacier at the mapped maximum position within paleoclimate constraints for cooling and drying, relative to present-day conditions. Assuming stable precipitation gradients, we model the Elwha glacier extent for the climates of these equilibria. At the warm end of the paleoclimate constraint (July average sea level temperature of 10.5 ∘C), a small valley glacier occurs in the high headwaters of the Elwha valley. Yet, for the cooler end of the allowable paleoclimate (July average sea level temperature of 7 ∘C), the Elwha glacier advances to a narrow notch in the valley, thickens, and rapidly extends far beyond the likely true maximum extent. Therefore, we suggest that the Early Wisconsin period was more likely to have been relatively warm because our models of the glacial extent are consistent with the past record of glaciation in both the Quinault valley and Elwha valley for warm conditions but inconsistent for cooler conditions. Alternatively, spatially variable drivers of ablation, including differences in cloudiness, could have contributed to past asymmetry in the glacier extent. Future research to constrain past precipitation gradients and evaluate their impact on glacier dynamics is needed to better interpret the climatic significance of past glaciation and to predict the future response of glaciers to climate change.

Density-dependent dynamics help explain the simultaneous expansion and decline of woodlands in the western US

Woodlands across the globe have expanded in recent centuries in response to shifts in climate, land use, and disturbance patterns, but are expected to be particularly vulnerable to decline as a result of environmental change. Gaps in our understanding of the drivers of woodland spatial dynamics, however, limit our ability to forecast range shifts. While the effect of extrinsic factors, such as fire and climate, on woodlands are well-documented, we lack knowledge on the role of intrinsic factors such as density-dependence in driving tree abundances and distributions across space and time. We used two remotely-sensed datasets of tree cover and climate predictors in Bayesian state-space models to test the effects of climate and density on the spatiotemporal changes in pinyon pine (Pinus monophylla) cover in the Great Basin, USA. We compared models with only climate predictors to models including both climate and tree cover (i.e. density-dependence). Models including density-dependence better explained observed dynamics than climate alone. Pinyon pine cover increased at the fastest rate in areas with intermediate precipitation and low maximum temperatures. Spatial effects of climate interacted with temporal variation in climate. In cool, wet areas, growth was higher in warm, dry periods. In warm, dry areas, it was favorable to have cool periods. However, positive direct effects of climate at mid to high elevation were partially offset by density-dependence. Because extant density was positively correlated with average precipitation and negatively correlated with temperature, pinyon pine cover often declined in climatically favorable areas. Our results indicate that direct effects of warming and drying climate will likely lead to increases in cover at high elevations and declines at low elevations in the long-term. However, the added effects of density-dependence may create counter-intuitive patterns with increases in low density, low elevation areas in the near term and declines in seemingly favorable high density cooler-wetter locations. As a result, management intended to increase woodland resilience through silvicultural treatments like density reductions may have the greatest impact at intermediate elevations where increasing climate stress coincides with high tree densities.

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.

Interannual climate variability improves niche estimates for ectothermic but not endothermic species

Climate is an important limiting factor of species’ niches and it is therefore regularly included in ecological applications such as species distribution models (SDMs). Climate predictors are often used in the form of long-term mean values, yet many species experience wide climatic variation over their lifespan and within their geographical range which is unlikely captured by long-term means. Further, depending on their physiology, distinct groups of species cope with climate variability differently. Ectothermic species, which are directly dependent on the thermal environment are expected to show a different response to temporal or spatial variability in temperature than endothermic groups that can decouple their internal temperature from that of their surroundings. Here, we explore the degree to which spatial variability and long-term temporal variability in temperature and precipitation change niche estimates for ectothermic (730 amphibian, 1276 reptile), and endothermic (1961 mammal) species globally. We use three different species distribution modelling (SDM) algorithms to quantify the effect of spatial and temporal climate variability, based on global range maps of all species and climate data from 1979 to 2013. All SDMs were cross-validated and accessed for their performance using the Area under the Curve (AUC) and the True Skill Statistic (TSS). The mean performance of SDMs using only climatic means as predictors was TSS = 0.71 and AUC = 0.90. The inclusion of spatial variability offers a significant gain in SDM performance (mean TSS = 0.74, mean AUC = 0.92), as does the inclusion of temporal variability (mean TSS = 0.80, mean AUC = 0.94). Including both spatial and temporal variability in SDMs shows the highest scores in AUC and TSS. Accounting for temporal rather than spatial variability in climate improved the SDM prediction especially in ectotherm groups such as amphibians and reptiles, while for endothermic mammals no such improvement was observed. These results indicate that including long term climate interannual climate variability into niche estimations matters most for ectothermic species that cannot decouple their physiology from the surrounding environment as endothermic species can.

Cardamom agro-environmental interrelationships analysis in Indian cardamom hills

The rainfall pattern seen in the Indian Cardamom Hills (ICH) has been extremely variable and complicated, with El Niño-Southern Oscillation (ENSO) playing a crucial role in shaping this pattern. In light of this, more investigation is required through improved statistical analysis. During the study period, there was greater variability in rainfall and the frequency of rainy days. About 2,730 mm of rainfall was reported in 2018, while the lowest amount (1168.3 mm) was registered for 2016. The largest decrease in decadal rainfall (&amp;gt;65 mm) was given by the decade 1960–1969, followed by 1980–1989 (&amp;gt;40 mm) and 2010–2019 (&amp;gt;10 mm). In the last 60 years of study, there has been a reduction of rainy days by 5 days in the last decade (2000–2009), but in the following decade (2010–2019), it registered an increasing trend, which is only slightly &amp;lt;2 days. The highest increase in decadal rainy days was observed for the 1970–1979 period. The smallest decadal increase was reported for the last decade (2010–2019). Total sunshine hours were the highest (1527.47) for the lowest rainfall year of 2016, while the lowest value (1,279) was recorded for the highest rainfall year (2021). The rainfall characteristics of ICH are highly influenced by the global ENSO phenomenon, both positively and negatively, depending on the global El Nino and La Nina conditions. Correspondingly, below and above-average rainfall was recorded consecutively for 1963–1973, 2003–2016, and 1970–2002. Higher bright forenoon sun hours occurred only during SWM months, which also reported maximum disease intensity on cardamom. The year 2016 was regarded as a poorly distributed year, with the lowest rainfall and the highest bright afternoon sun hours during the winter and summer months (January-May). Over the last three decades, the production and productivity of cardamom have shown a steady increase along with the ongoing local climatic change. Many of our statistical tests resulted in important information in support of temporal climatic change and variability. Maintaining shade levels is essential to address the adverse effects of increasing surface air temperature coupled with the downward trend of the number of rainy days and elevated soil temperature levels.

Forest tree species adaptation to climate across biomes: Building on the legacy of ecological genetics to anticipate responses to climate change.

Intraspecific variation plays a critical role in extant and future forest responses to climate change. Forest tree species with wide climatic niches rely on the intraspecific variation resulting from genetic adaptation and phenotypic plasticity to accommodate spatial and temporal climate variability. A centuries-old legacy of forest ecological genetics and provenance trials has provided a strong foundation upon which to continue building on this knowledge, which is critical to maintain climate-adapted forests. Our overall objective is to understand forest trees intraspecific responses to climate across species and biomes, while our specific objectives are to describe ecological genetics models used to build our foundational knowledge, summarize modeling approaches that have expanded the traditional toolset, and extensively review the literature from 1994 to 2021 to highlight the main contributions of this legacy and the new analyzes of provenance trials. We reviewed 103 studies comprising at least three common gardens, which covered 58 forest tree species, 28 of them with range-wide studies. Although studies using provenance trial data cover mostly commercially important forest tree species from temperate and boreal biomes, this synthesis provides a global overview of forest tree species adaptation to climate. We found that evidence for genetic adaptation to local climate is commonly present in the species studied (79%), being more common in conifers (87.5%) than in broadleaf species (67%). In 57% of the species, clines in fitness-related traits were associated with temperature variables, in 14% of the species with precipitation, and in 25% of the species with both. Evidence of adaptation lags was found in 50% of the species with range-wide studies. We conclude that ecological genetics models and analysis of provenance trial data provide excellent insights on intraspecific genetic variation, whereas the role and limits of phenotypic plasticity, which will likely determine the fate of extant forests, is vastly understudied.