Climatic Drivers Research Articles

Campanian Ignimbrite tephra reveals asynchronous vegetation responses to abrupt climate change in the eastern Mediterranean region

The timing and rate of ecosystem response to abrupt climate change is a product of numerous complex interactions between biotic and abiotic drivers. Palaeoecological studies from long sedimentary records, particularly those that span periods of dynamic climate such as the last glacial cycle, can help to contextualise ecosystem responses to climate variability through time. Detailed studies that compare proxy data from multiple sites, with high chronological precision, have the potential to ascribe mutual climate drivers, and, therefore, track spatiotemporal variability in ecosystem responses. Here, we interrogate the vegetation impact of past climate change in the eastern Mediterranean, using three sub-centennially resolved pollen archives from Greece. The widespread Campanian Ignimbrite (CI/Y-5; ca. 39.85 ka BP) tephra marker is used as an isochron to directly correlate pollen records from Ioannina (NW Greece), Tenaghi Philippon (NE Greece), and Megali Limni (NE Aegean). Our results reveal spatiotemporal variability in the timing of vegetation response in the Mediterranean to climate forcing across Heinrich Stadial 4 (40.2–38.3 ka BP), a period of known abrupt climatic change. We identify a decline in tree pollen in all three sites, likely related to the onset of enhanced regional aridity, with vegetation at Tenaghi Philippon responding prior to the CI/Y-5, in contrast to at Megali Limni and Ioannina, where much of the vegetation change occurs following tephra deposition.

Spatial patterns and climatic drivers of phylogenetic structure of regional liverwort assemblages in China.

Latitudinal diversity gradients have been intimately linked to the tropical niche conservatism hypothesis, which posits that there has been a strong filter due to the challenges faced by ancestral tropical lineages to adapt to low temperatures and colonize extra-tropical regions. In liverworts, species richness is higher towards the tropics, but the centres of diversity of the basal lineages are distributed across extra-tropical regions, pointing to the colonization of tropical regions by phylogenetically clustered assemblages of species of temperate origin. Here, we test this hypothesis through analyses of the relationship between macroclimatic variation and phylogenetic diversity in Chinese liverworts. Phylogenetic diversity metrics and their standardized effect sizes for liverworts in each of the 28 regional floras at the province level in China were related to latitude and six climate variables using regression analysis. We conducted variation partitioning analyses to determine the relative importance of each group of climatic variables. We find that the number of species decreases with latitude, whereas phylogenetic diversity shows the reverse pattern, and that phylogenetic diversity is more strongly correlated with temperature-related variables compared with precipitation-related variables. We interpret the opposite patterns observed in phylogenetic diversity and species richness in terms of a more recent origin of tropical diversity coupled with higher extinctions in temperate regions.

Regional divergent evolution of vegetation greenness and climatic drivers in the Sahel-Sudan-Guinea region: nonlinearity and explainable machine learning

IntroductionThe vegetation dynamics of the Sahel-Sudan-Guinea region in Africa, one of the largest transition zones between arid and humid zones, is of great significance for understanding regional ecosystem changes. However, a time-unvarying trend based on linear assumption challenges the overall understanding of vegetation greenness evolution and of tracking a complex ecosystem response to climate in the Sahel-Sudan-Guinea region.MethodsThis study first applied the ensemble empirical mode decomposition (EEMD) method to detect the time-varying trends in vegetation greenness based on normalized difference vegetation index (NDVI) data in the region during 2001–2020, and then identified the dominant climatic drivers of NDVI trends by employing explainable machine learning framework.ResultsThe study revealed an overall vegetation greening but a significant nonlinear spatio-temporal evolution characteristic over the region. Trend reversals, i.e., browning-to-greening and greening-to-browning, were dominant in approximately 60% of the study area. The browning-to-greening reversal was primarily observed in the southern Sahel, Congo Basin north of the Equator, and East Africa, with a breakpoint around 2008, while the greening-to-browning reversal was mainly observed in West Africa, with a breakpoint around 2011. The sustained greening primarily took place in northern Sahel, Central African Republic and South Sudan; while sustained browning clustered in central West Africa and Uganda, mainly in agricultural lands. Furthermore, the combination of Random Forest (RF) algorithm and the SHapley Additive exPlanations (SHAP) method could robustly model and reveal the relationships between the observed trends in NDVI and in climatic variables, also detected by applying EEMD. The results suggested that air temperature and precipitation were the most important climatic drivers controlling the NDVI trends across the Sahel-Sudan-Guinea region. The NDVI trends were more likely to have negative correlations with solar radiation and vapor pressure deficit in arid areas, while they could have positive correlations in humid areas. The study also found that large-scale climate changes induced by sea surface temperature (SST) anomalies had strong relationships with trend reversals in vegetation greenness at a sub-continental scale. These findings advanced the understanding of the impacts of climatic drivers on vegetation greenness evolution in the Sahel-Sudan-Guinea region.

Spatial heterogeneity and driving mechanisms of carbon storage in the urban agglomeration within complex terrain: Multi-scale analyses under localized SSP-RCP narratives

Ecological carbon storage is critical to climate change mitigation by absorbing CO2 from the atmosphere. Mountainous ecosystems, crucial carbon reservoirs, are experiencing persistent carbon storage declines, yet management mechanisms targeting such complex terrains remain challenging. Focusing on the Sichuan-Chongqing urban agglomeration, interwoven as mountains and basins, we employed the MPI-coupled (Improved Markov-PLUS-InVEST) model and localized "socio-economic-climatic" narratives to simulate the dynamic evolution of carbon storage over extended periods. Diverse impacts and gradient effects of social, economic, and natural (incl. terrain, climate) drivers on carbon storage are further examined. The findings revealed that: (1) The most carbon storage growth projected from 2030 to 2060 reaches 1.53 %, but of which localized SSP5–8.5 narrative the urbanization encroached upon the undeveloped mountainous, severely impairing carbon sequestration by 81.5Tg. (2) In addition to landscape types, carbon storage exhibits complex spatial responses to economic development, population size, and terrain with an inverted U-shaped relationship. The proposed methodology offers more accurate carbon storage estimates and assists policy-makers in similar terrains to effectively manage the spatial heterogeneity of carbon storage. This study facilitates the implementation of tailored ecological conservation through urban zoning, thus contributing to enhanced subregional climate change mitigation efforts globally.

Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m2/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.

Connections between soil microbes, land use and European climate: Insights for management practices

Soil microbial biomass and activity strongly depend on land use, vegetation cover, climate, and soil physicochemical properties. In most cases, this dependence was assessed by one-to-one correlations while by employing network analysis, information about network robustness and the balance between stochasticity and determinism controlling connectivity, was revealed. In this study, we further elaborated on the hypothesis of Smith et al. (2021) that cropland soils depended more on climate variables and therefore are more vulnerable to climate change. We used the same dataset with that of Smith et al. (2021) that contains seasonal microbial, climate and soil variables collected from 881 soil points representing the main land uses in Europe: forests, grassland, cropland. We examined complete (both direct and indirect relationships) and incomplete networks (only direct relationships) and recorded higher robustness in the former. Partial Least Square results showed that on average more than 45% of microbial attributes' variability was predicted by climate and habitat drivers denoting medium to strong effect of habitat filtering. Network architecture slightly affected by season or land use type; it followed the core/periphery structure with positive and negative interactions and no hub nodes. Microbial attributes (biomass, activity and their ratio) mostly belong to core block together with Soil Organic Carbon (SOC), while climate and soil variables to periphery block with the exception of cropland networks, denoting the higher dependence between microbial and climate variables in these latter. All complete networks appeared robust except for cropland and forest in summer, a finding that disagrees with our initial hypothesis about cropland. Networks' connectivity was controlled stronger by stochasticity in forest than in croplands. The lack of human interventions in forest soils increase habitat homogeneity enhancing the influence of stochastic agents such as microbial unlimited dispersal and/or stochastic extinction. The increased stochasticity implies the necessity for proactive management actions.

Climate, wildfire, and volcanic ash drivers of ecosystem change in high mountain forests, British Columbia, Canada

Northwest North America has unique high elevation Picea–Abies forests and parkland classified in British Columbia as the Engelmann Spruce–Subalpine–fir (ESSF) Biogeoclimatic zone. These ecosystems occur on a topographically and climatically complex landscape, juxtaposed with diverse vegetation types including alpine tundra, inland rain forest, dry conifer forest, and grasslands. Spatio-temporal ecosystem disturbance is varied, driven by factors such as climate variation, wildfire, volcanic eruptions, and insect herbivory. A pollen and charcoal record derived from a lake sediment core from the ESSF reveals a unique late-glacial to modern vegetation history progressing from alpine steppe through dry open conifer forest to moist spruce–fir ecosystems, the latter arising only 4600 years ago, late by comparison to other ESSF sites in the region. Repeated disturbance in the mid Holocene by wildfire coupled with volcanic ash deposition and increased climatic variation resulted in recurring Pinus contorta-dominated seral forest stands before cooling and moistening in the late Holocene led to stable Picea–Abies forest. With rapid climate change, changing disturbance regimes, and timber harvest, the management of dry ESSF forests needs to consider that this forest-type could transform into parkland or open seral pine stands, with a high frequency disturbance regime.

Investigation of the Historical Trends and Variability of Rainfall Patterns during the March–May Season in Rwanda

This study explores the spatiotemporal variability and determinants of rainfall patterns during the March to May (MAM) season in Rwanda, incorporating an analysis of teleconnections with oceanic–atmospheric indices over the period 1983–2021. Utilizing the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset, the study employs a set of statistical tools including standardized anomalies, empirical orthogonal functions (EOF), Pearson correlation, the Mann–Kendall (MK) trend test, and Sen’s slope estimator to dissect the intricacies of rainfall variability, trends, and their association with large-scale climatic drivers. The findings reveal a distinct southwest to northwest rainfall gradient across Rwanda, with the MK test signaling a decline in annual precipitation, particularly in the southwest. The analysis for the MAM season reveals a general downtrend in rainfall, attributed in part to teleconnections with the Indian Ocean Sea surface temperatures (SSTs). Notably, the leading EOF mode for MAM rainfall demonstrates a unimodal pattern, explaining a significant 51.19% of total variance, and underscoring the pivotal role of atmospheric dynamics and moisture conveyance in shaping seasonal rainfall. The spatial correlation analysis suggests a modest linkage between MAM rainfall and the Indian Ocean Dipole, indicating that negative (positive) phases are likely to result in anomalously wet (dry) conditions in Rwanda. This comprehensive assessment highlights the intricate interplay between local rainfall patterns and global climatic phenomena, offering valuable insights into the meteorological underpinnings of rainfall variability during Rwanda’s critical MAM season.

Millennial-scale climate cycles modulated by Milankovitch forcing in the middle Cambrian (ca. 500 Ma) Marjum Formation, Utah, USA

Abstract Middle Cambrian offshore deposits of the Marjum Formation, Utah, USA, are characterized by four scales of superimposed cyclicity defined by varying fine siliciclastic versus limestone abundances; these include limestone-marl couplets (rhythmites; 5–10 cm), which are bundled into parasequences (1–2 m) and small-scale (5–10 m) and large-scale (20–40 m) sequences. Time series analysis of SiO2 and lithologic rank stratigraphic series reveal cycles consistent with Milankovitch periods corresponding to Cambrian orbital eccentricity (20 m, 405 k.y.; 6 m, 110 k.y.), obliquity (1.8 m, 30 k.y.), climatic precession (1.15 m, 18 k.y.), and half-precession (0.64 m, 7 k.y.). Astronomical calibration of the lithologic rank series indicates that the main sub-Milankovitch cycle at 0.065 m represents ~1 k.y. and corresponds to the basic rhythmite couplet. All scales of cyclicity are interpreted as the result of wet versus dry monsoonal climate oscillations controlling the abundance of fine siliciclastic sediment influx to the basin. A plausible millennial-scale climate driver is solar activity. These results describe one of the oldest known geological candidates for solar-influenced climate change modulated by Milankovitch forcing.

Global Flash Droughts Characteristics: Onset, Duration, and Extent at Watershed Scales

AbstractAddressing impacts of flash droughts (FDs) on the water‐food nexus requires a understanding of FD mechanisms and drivers at the watershed level. Examining climatic drivers, dry and wet spell lengths from 1980 to 2019, we analyzed FD spatial and temporal characteristics, emphasizing areal extent, onset time, and duration. Our findings reveal substantial variations in FDs among different watersheds. Notably, watersheds in the Southern Hemisphere are witnessing expanding, faster‐developing, and longer‐lasting FDs, aligning with climate variations in precipitation and temperature. Additionally, at the watershed scale, the onset and duration of FDs are more influenced by the intensity (magnitude and variability) of climatic drivers than the average length of wet and dry periods. FD‐extents, however, correlate with both climatic conditions and wet and dry periods, underscoring watershed connectivity. Ultimately, our results underscore the necessity for research to comprehend the interplay between FDs and watershed characteristics and how it manifests in overall water resource management.

Tracking states and transitions in semiarid rangelands: A spatiotemporal archetypal analysis of productivity dynamics using wavelets

Climate change poses challenges in classifying ecosystem dynamics, as they are influenced by shifting dynamics resulting from changes in climate forces and meteorological variables, including temperature and water availability. To address this, our study presents a novel approach using Continuous Wavelet Transform (CWT) and power spectrum analysis to classify vegetation dynamics, considering the time-dependent variability of ecosystem frequencies. We applied our method to centred and standardized MODIS NDVI time series for the period 2000–2021, using an experimental field station in northern Patagonia as a case study. By performing a continuous wavelet transform on the data for each pixel, we obtained instantaneous power spectra, capturing variability across different dates and pixels. These spectrums were then consolidated into a comprehensive database, and subsequently classified using archetypal analysis. We identified a convex combination of archetypal spectrums that best represented the entire power spectrum database. Mapping the resulting archetypes and their weights in both space and time allowed us to explore pixels' variations in archetype weights in relation to factors such as time, topography, and climate. In addition, to examine the potential relationship between the NDVI time series and climate drivers, we computed the Average Cross-Wavelet Power Spectrum (ACWPS) to different climatic indices. Three archetypes were sufficient to explain the majority of power spectrum variability in the study area. These archetypes exhibited distinctive characteristics: 1) medium-frequency variability (2–4 years), 2) low-frequency variability (>4 years), and 3) an annual (i.e. seasonal) cycle with low-frequency variability. Spatially, the first two archetypes were predominantly observed in highland steppes, while the third archetype prevailed in lowland areas associated with meadows. At the beginning of the studied period, Archetypes 1 and 3 dominated, but after the Puyehue-Cordón Caulle Volcanic Complex eruption in 2011 their prominence diminished, and Archetype 2 became more prevalent in the whole study area. Finally, all three NDVI series representative of archetypes showed a relative peak at approximately four years, which could be linked to the Indian Ocean Dipole variability. These results highlight an abrupt shift in the system's behaviour, primarily related to changes in variability distribution rather than mean values. This disturbance-induced transition aligns with the theory of state and transitions in ecological system dynamics. We propose that the states in this model are not fixed but represent alternative dynamic behaviours, akin to different types of limit cycles. Consequently, employing a wavelet analysis-based classification method provides a robust means of studying and understanding such variability and transitions, thereby offering clarity and comprehension of ecosystem states. Notably, this methodology proves particularly effective for large databases of detailed time series.

Historical climate drivers and species’ ecological niche in the Beaufort Sea food web

Abstract Climate change impacts have been particularly acute and rapid in the Arctic, raising concerns about the conservation of key ecologically and culturally significant species (e.g. beluga whales, Arctic cod), with consequences for the Indigenous community groups in the region. Here, we build on an Ecopath with Ecosim model for the Canadian Beaufort Sea Shelf and Slope to examine historical (1970–2021) changes in the ecological dynamics of the food web and key species under climate change. We compare the individual and cumulative effects of (i) increased sea surface temperature; (ii) reduced sea ice extent; (iii) ocean deoxygenation; and (iv) changing ocean salinity in the ecosystem models. We found that including salinity time series in our ecosystem models reduced the diversity found within the ecosystem, and altered the trophic levels, biomass, and consumption rates of some marine mammal and fish functional groups, including the key species: beluga whales, as well as Arctic and polar cods. Inclusion of the dissolved oxygen time series showed no difference to ecosystem indicators. The model findings reveal valuable insights into the attribution of temperature and salinity on Arctic ecosystems and highlight important factors to be considered to ensure that existing conservation measures can support climate adaptation.

Holocene vegetation, fire, climate dynamics and human impact in the upper Orkhon Valley of the Khangai Mountains, Mongolia

The Upper Orkhon Valley, located in the Khangai Mountains of Central Mongolia, is mainly covered by a forest-steppe ecosystem. This is an interesting area to study because its vegetation dynamics might have been influenced by climatic and anthropogenic drivers. This study investigates the dynamics of vegetation (pollen), human activity (pollen and NPP) and fire activity (macro-charcoal) from a lacustrine sediment core from lake Shireet Naiman Nuur, covering the last 7400 cal yr BP. The results of the pollen analysis showed a continual and constant decline of tree species in favour of the development of grasslands. Fire activity was more intense (high peak magnitude) during the mid Holocene, while it became more frequent but less intense from 5000 cal yr BP onwards, when herbaceous taxa became more present. The NPP analysis suggest that the study site was free of human activity from 7400 to 3500 cal yr BP, whereas the traces of the development of human societies that took place in the Orkhon Valley, such as pastoralism and grazing activities, have been demonstrated by the increase in coprophilous fungi and the Poaceae Cerealia-type pollen around 3200 cal yr BP, although its impact seems to have remained low.

Using luminescence dating to constrain lake sediment records: A new age model for the 1.38 Ma lake Malawi drill core, Eastern Africa

The 2005 Lake Malawi deep scientific drill core is the longest and most continuous high-resolution record from the continental tropics, extending to ∼1.38 Ma. While extensive sets of paleoclimate proxy data have been generated from this core, a gap in directly dated sediment, between ∼74 ka and ∼590 ka (28–167 m below lake floor), has limited the understanding of climatic drivers in this system. Previous age models fill the gap in direct dates by tuning to the global δ18O stack and interpreting paleomagnetic excursions, but these methods and the resulting models remain disputed. We fill this gap in chronology using luminescence dating, with 31 samples collected at ∼4 m resolution in this section of the core. Luminescence dating can sometimes be limited by relatively large uncertainties (typically ∼7%) and difficulty estimating the water content history of samples. We overcome these limitations by employing a high sampling density and using the sediment record to understand changes in water content during the burial period. This yields a vastly improved and robust age model that indicates changes in sedimentation rates not discernible in prior age models.

Modelling non-stationarity in extreme rainfall using large-scale climate drivers

Flood estimates used in engineering design are commonly based on intensity–duration–frequency (IDF) curves derived from historical extreme rainfalls. Under global warming these extreme rainfalls are increasing, threatening the capacity of existing infrastructure to resist failure as IDF curves traditionally assume no change in rainfall magnitude. Hence, there is a need to investigate the implications of non-stationarity in extreme rainfalls used to derive IDFs across storm durations and annual exceedance probabilities (AEPs). One way of doing this is to incorporate covariates into the fitted probability distribution. However, there are few studies which examine non-stationarity in extreme rainfall using large-scale climate drivers as covariates, with little consensus on which covariate is the most appropriate. Here we evaluate non-stationarity in extreme rainfall across different durations, from the 1 in 5 AEP to the 1 in 100 AEP, using potential large-scale climate drivers including global and continental mean temperature, continental diurnal temperature range, continental dewpoint temperature, continental precipitable water, the Indian Ocean Dipole, the El Niño Southern Oscillation, and the Southern Annular Mode. These covariates are linked to the three parameters of the Generalized Extreme Value distribution to identify the most appropriate form of non-stationary probability model. We analyse 16 different storm durations from 6 min to 7 day annual maxima across 46 stations in Australia. Based on the Akaike Information Criteria, precipitable water is the superior covariate at a large proportion of stations irrespective of storm duration. However, when the modelled quantile changes are inspected, only global temperature is able to adequately capture the variability in changes across both storm duration and annual exceedance probability. Further, when regional average values of mean temperature, diurnal temperature range, dewpoint temperature and precipitable water were used as covariates, there was no improvement in the model performance compared to continental-wide covariates, particularly for short durations. The results using a mean global temperature covariate show that stationary historical rainfall quantile estimates are underestimated by 12 % – 9 % for frequent (1 in 5 AEP) and 23 % – 13 % for rare (1 in 100 AEP) short duration (6 min – 30 min) events. Moving forward our results suggests infrastructure design needs to incorporate non-stationarity in IDFs to ensure infrastructure and flood planning levels are not under-designed.

Climate drivers of litterfall biomass dynamics in three types of forest stands on the Loess Plateau

Litterfall is a link in the energy flow and material cycling of ecosystems, which maintain the primary productivity of forests. However, there is no consensus regarding the factors driving for the litterfall biomass dynamics because of the high spatial–temporal heterogeneity. Herein, we investigated and compared the stand litter biomass, litterfall biomass, including its components in the pure Robinia pseudoacacia forest (RL), pure Platycladus orientalis forest (PL), and mixed forest of Robinia pseudoacacia and Platycladus orientalis (ML) from 2020 to 2022 in the gully erosion area of the Loess Plateau. Correlation and multiple regression analyses were used to study the relationships among the amount of stand litter, litterfall biomass, and seasonal temperature and precipitation. Significant variation was found in the seasonal and annual litterfall biomass of the three types of forest stands. Annual stand litter biomass was in the order PL > RL > ML, whereas the annual litterfall biomass was in the order RL > ML > PL. The seasonal dynamics of litterfall biomass of twigs, leaf and fruits exhibited a ‘unimodal pattern’ with a peak in October. Annual leaf litterfall biomass formed, the main body of litter in all the components, accounting for 57.14–67.05 % of the total litterfall biomass. The correlation and regression analysis results showed that the stand litter biomass was primarily affected by the average temperature. Total litterfall biomass and twigs, leaf, and fruits were significantly affected by the precipitation and the maximum temperature, however, flower litterfall biomass was mainly affected by the average temperature. Overall, our results indicated that temperature plays a key role in the climatic factors affecting forest litterfall processes, while different climatic conditions and biological characteristics probably caused the differences in litterfall biomass. In the long term, temperature will potentially play a leading role in altering carbon storage and nutrient cycling as the climate warms.

Changes in terrestrial water storage in the Three-North region of China over 2003–2021: Assessing the roles of climate and vegetation restoration

Large-scale vegetation restoration has markedly enhanced ecosystem services in the Three-North (TN) region, concurrently exacerbating the pre-existing water resource crisis stemming from climate change in this arid domain. However, the specific contributions of climate and vegetation changes to the evolution of water resources in the TN region have yet to be elucidated. This study leverages data from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-on mission (GRACE-FO), specifically the terrestrial water storage anomaly (TWSA) from 2003 to 2021. We identify two TWSA rising (TWSA_rⅠ and TWSA_rⅡ) and two declining (TWSA_dⅠ and TWSA_dⅡ) hotspots in the TN region. By exploring the relative importance of climate drivers and vegetation dynamics in influencing the temporal dynamics of TWSA, we found that vegetation restoration assumes a more pivotal role than climate factors in 45.2% of the total area within the identified hotspots, predominantly evident in TWSA declining hotspots. The heightened TWSA in TWSA_rⅠ and TWSA_rⅡ primarily stems from precipitation recharge, with precipitation-dominated areas constituting 19.3% of the area within the identified hotspots. Potential evapotranspiration dominates TWSA changes in only 4.6% of the region within the identified hotspots, primarily scattered in TWSA_dⅠ and TWSA_dⅡ. Furthermore, a comparative assessment of soil moisture and groundwater responses to environmental factors indicates that water storage at shallower layers is more responsive to climate factors, while the impacts of vegetation changes on TWSA are more discernibly reflected in water storage at deeper layers. These findings furnish scientific guidance for future ecological restoration planning and sustainable water resources management.

Analisis Of Upgrading Low-Crested Structures As An Adaptation Measure To Climate Change For Coastal Protection: A Hybrid Approach

Coastal zones have consistently been among the most appealing settlement areas due to their proximity to the sea, rich natural resources, and the high quality of life they offer (Lamberti et al., 2005). However, these regions are affected by climate change impacts, such as sea-level rise, storm surges, and an increased intensity of extreme weather events (Burcharth et al., 2014). Traditional low-crested rubble mound breakwaters are commonly used to protect coastal areas from wave damage. However, it is expected that the variability of climate conditions will induce a loss of functionality and structural integrity in the coming decades. Coastal communities and coastal managers, within the framework of sustainable development, are demanding new approaches that include not only the preservation of the hydraulic performance of the breakwaters, but also other factors, including social and environmental impacts. These requests significantly affect the traditional way to conceive those structures to be integrated into the coastal landscape. To cope with the variation of climate drivers, existing low-crested breakwaters must be adapted to accommodate social demands and environmental issues. Therefore, upgrading and maintaining the existing rubble-mound breakwater is a hot topic in coastal engineering, and deserves special attention due to the possible intensification of external loads resulting from the impacts of climate change (Stagnitti et al., 2023). Upgrading can be done by modifying the structure profile and/or adding structure elements (Burcharth et al., 2014). In 2011, Cappietti provided curves for the functional design of submerged breakwaters to be used in place of preexisting emergent breakwaters. Burcharth et al. (2014) explained that the best way to improve the structure is to put an additional layer of protection on the front slope, as long as the foreshore has a mild slope of around 1:100. But if the foreshore becomes steeper because of erosion, then a front berm will also be needed. Stagnitti et al. (2022) introduced a novel methodology based on the calculation of the failure probability during a lifetime due to independent failure modes. Their method was employed to evaluate the performance of upgraded breakwaters in response to climate change. Estimating the wave overtopping of both existing and upgraded breakwaters is essential for designing upgrade options that can ensure the safety of port operations. Stagnitti et al. (2023) applied the numerical model IH2VOF, which was calibrated using experimental data, to study the wave overtopping of damaged and upgraded rubble-mound breakwaters. The present work examines the feasibility of converting emerged rubble-mound breakwaters into submerged breakwaters to minimize their detrimental environmental effects. To achieve this goal, an investigation was conducted to evaluate the hydraulic performance of submerged breakwaters that are created by lowering the crest of existing emerged breakwaters. Additionally, two-dimensional numerical simulations were performed using IH2VOF to investigate wave interactions with the structures.

Examining the effects of climate change and human impacts on a high-resolution, late Holocene paleofire record from South Africa's winter rainfall zone

Fire is central to the Cape Floristic Region's highly biodiverse and disturbance-adapted Fynbos Biome. However, prehistoric fire regimes, their ecological consequences, and their relationships with large-scale climate drivers and human activities remain poorly understood. Here, we use a high-resolution sedimentary charcoal record from Verlorenvlei, a coastal lake situated on the west coast, to interrogate links between fire, climate, and pastoralism in the Fynbos Biome. Our record has a robust chronology supported by 24 radiocarbon dates and provides a continuous sedimentary sequence spanning the last 4200 years, documenting fire activity before and after the local arrival of pastoralists in the Verlorenvlei area ∼1500 cal years BP. Fire at Verlorenvlei over the last 4200 years is variable, with relatively low activity until ∼2000 cal years BP, after which variable but generally higher fire activity occurs until the highest period of fire activity from ∼1450 to 1800 CE (∼500–150 cal years BP). The increase in fire activity ∼2000 years ago corresponds with a shift in the diatom assemblage at Verlorenvlei from marine towards brackish and freshwater species, reflecting increased precipitation derived from a strengthening of the southern westerly winds. The peak in fire activity beginning ∼1450 CE (∼500 cal years BP), near the onset of the Little Ice Age, tracks a second diatom-inferred strengthening of the westerly winds. Other southern hemisphere and Antarctic records further corroborate this increased westerly influence after ∼2000 years. Linear regression modeling on the fire record indicates that moisture availability is the primary driver of fire at Verlorenvlei, with little evidence that human populations influenced fire. Our reconstruction suggests that fire activity at Verlorenvlei is limited by moisture availability and that wetter conditions facilitate increased vegetation (i.e., fuel) and intensified fire at this otherwise fuel-limited site. This work has implications for management and conservation decisions in response to future predictions of a warmer and drier climate along South Africa's west coast.

Substantial variation of the two‐dimensional spectrum of senescent leaf in subalpine forest along elevational gradients

AbstractLeaf senescence have shed light on its contribution to the recovery of nutrients and signaling of the transition from the active to the dormant stage in winter. The manner by which climatic and edaphic factors and plant phylogeny affect the senescent leaf economics spectrum (more mineral elements included) of different growth habits along elevational gradients in mountain ecosystems is highly uncertain. To fill this knowledge gap, we examined 15 representative subalpine forest species' senescent leaf functional traits (i.e., specific leaf area [SLA], leaf dry matter content [DMC], pH, carbon [C], nitrogen [N], phosphorus [P], potassium [K], calcium [Ca], sodium [Na], magnesium [Mg], manganese [Mn], aluminum [Al], iron [Fe], C:N, N:P, and C:P ratios) to determine the senescent leaf economics spectrum along elevational gradients in the Hengduan Mountains, China. Our results demonstrate that all the functional traits of senescent leaf showed strong correlations with one another, and K was observed as the hub for the leaf economics spectrum. Irrespective of the difference among growth habits (arbor vs. shrub), leaf habits (deciduous vs. evergreen), and leaf types (broad vs. needle), the variations in the two‐dimensional spectrum of the senescent leaf trait exhibited uniformly positive associations with elevation, primarily attributed to climatic drivers. However, the modulation of the senescent leaf economics spectrum by soil properties and plant phylogeny was surprisingly modest. Collectively, our results revealed that the resource trade‐off of senescent leaf showed a trend from acquisition to conservation along elevational gradients, which could broaden the plant economics spectrum to include senescent leaf.