High-resolution Climate Data Research Articles

Past, present and future rainfall erosivity in central Europe based on convection-permitting climate simulations

Abstract. Heavy rainfall is the main driver of soil erosion by water, which is a threat to soil and water resources across the globe. As a consequence of climate change, precipitation – especially extreme precipitation – is increasing in a warmer world, leading to an increase in rainfall erosivity. However, conventional global climate models struggle to represent extreme rain events and cannot provide precipitation data at the high spatiotemporal resolution that is needed for an accurate estimation of future rainfall erosivity. Convection-permitting simulations (CPSs), on the other hand, provide high-resolution precipitation data and a better representation of extreme rain events, but they are mostly limited to relatively small spatial extents and short time periods. Here, we present, for the first time, rainfall erosivity in a large modeling domain such as central Europe based on high-resolution CPS climate data generated with the regional climate model COSMO-CLM using the Representative Concentration Pathway 8.5 (RCP8.5) emission scenario. We calculated rainfall erosivity for the past (1971–2000), present (2001–2019), near future (2031–2060) and far future (2071–2100). Our results showed that future increases in rainfall erosivity in central Europe can be up to 84 % in the region's river basins. These increases are much higher than previously estimated based on regression with mean annual precipitation. We conclude that despite remaining limitations, CPSs have an enormous and currently unexploited potential for climate impact studies on soil erosion. Thus, the soil erosion modeling community should closely follow the recent and future advances in climate modeling to take advantage of new CPSs for climate impact studies.

Downscaling air temperatures for high-resolution niche modeling in a valley of the Amazon lowland forests: A case study on the microclima R package.

The forests of the Amazon basin are threatened by climate and land use changes. Due to the transition towards a drier climate, moisture-dependent organisms such as canopy epiphytes are particularly affected. Even if the topography in the Amazon lowland is moderate, mesoscale nocturnal katabatic flows result from cold air production related to radiative cooling. From a certain level of mass the cold air starts to flow downslope towards the valley centers leading to temperature inversions. The resulting cooling in the valleys drives localized fog formation in the valleys at night. This correlates with high epiphyte abundance and diversity in the valleys, which is much less pronounced upslope. The underlying temperature dynamics are, however, not sufficiently included in coarse-resolution reanalysis models such as ERA5-Land. Since high resolution climate data are needed e.g. for proper niche modeling of locally distributed species such as canopy epiphytes, downscaling models such as microclima have been developed and include micro- and mesoscale effects. However, it is unclear how well the elevation-related diurnal course of air temperature can be simulated. Here, we test functions for downscaling coarse-resolution temperature data to high spatial resolution data implemented in the R-package microclima for the South American tropical lowland forests. To do so we compared microclima-downscaled ERA5-Land air temperature data with meteorological station data. We found that the microclima functions only properly detect 73 temperature inversions out of 412 nocturnal cold air drainage (CAD) events during the dry season study period and only 18 out of 400 during the wet season with default settings. By modifying default values such as the emissivity threshold and time frames of possible CAD condition detection, we found 345 of 412 CAD events during the dry season and 177 out of 400 during the wet season. Despite problems with the distinction between CAD and non-CAD events the microclima algorithms show difficulties in correctly modeling the diurnal course of the temperature data and the amplitudes of elevational temperature gradients. For future studies focusing on temperature downscaling approaches, the modules implemented in the microclima package have to be adjusted for their usage in tropical lowland forest studies and beyond.

Species distribution modeling for disease ecology: A multi-scale case study for schistosomiasis host snails in Brazil.

Species distribution models (SDMs) are increasingly popular tools for profiling disease risk in ecology, particularly for infectious diseases of public health importance that include an obligate non-human host in their transmission cycle. SDMs can create high-resolution maps of host distribution across geographical scales, reflecting baseline risk of disease. However, as SDM computational methods have rapidly expanded, there are many outstanding methodological questions. Here we address key questions about SDM application, using schistosomiasis risk in Brazil as a case study. Schistosomiasis is transmitted to humans through contact with the free-living infectious stage of Schistosoma spp. parasites released from freshwater snails, the parasite's obligate intermediate hosts. In this study, we compared snail SDM performance across machine learning (ML) approaches (MaxEnt, Random Forest, and Boosted Regression Trees), geographic extents (national, regional, and state), types of presence data (expert-collected and publicly-available), and snail species (Biomphalaria glabrata, B. straminea, and B. tenagophila). We used high-resolution (1km) climate, hydrology, land-use/land-cover (LULC), and soil property data to describe the snails' ecological niche and evaluated models on multiple criteria. Although all ML approaches produced comparable spatially cross-validated performance metrics, their suitability maps showed major qualitative differences that required validation based on local expert knowledge. Additionally, our findings revealed varying importance of LULC and bioclimatic variables for different snail species at different spatial scales. Finally, we found that models using publicly-available data predicted snail distribution with comparable AUC values to models using expert-collected data. This work serves as an instructional guide to SDM methods that can be applied to a range of vector-borne and zoonotic diseases. In addition, it advances our understanding of the relevant environment and bioclimatic determinants of schistosomiasis risk in Brazil.

Incorporating high-resolution climate, remote sensing and topographic data to map annual forest growth in central and eastern Europe

To enhance our understanding of forest carbon sequestration, climate change mitigation and drought impact on forest ecosystems, the availability of high-resolution annual forest growth maps based on tree-ring width (TRW) would provide a significant advancement to the field. Site-specific characteristics, which can be approximated by high-resolution Earth observation by satellites (EOS), emerge as crucial drivers of forest growth, influencing how climate translates into tree growth. EOS provides information on surface reflectance related to forest characteristics and thus can potentially improve the accuracy of forest growth models based on TRW. Through the modelling of TRW using EOS, climate and topography data, we showed that species-specific models can explain up to 52 % of model variance (Quercus petraea), while combining different species results in relatively poor model performance (R2 = 13 %). The integration of EOS into models based solely on climate and elevation data improved the explained variance by 6 % on average. Leveraging these insights, we successfully generated a map of annual TRW for the year 2021. We employed the area of applicability (AOA) approach to delineate the range in which our models are deemed valid. The calculated AOA for the established forest-type models was 73 % of the study region, indicating robust spatial applicability. Notably, unreliable predictions predominantly occurred in the climate margins of our dataset. In conclusion, our large-scale assessment underscores the efficacy of combining climate, EOS and topographic data to develop robust models for mapping annual TRW. This research not only fills a critical void in the current understanding of forest growth dynamics but also highlights the potential of integrated data sources for comprehensive ecosystem assessments.

A hydrogen isoscape for tracing the migration of herbivorous lepidopterans across the Afro-Palearctic range.

Many insect species undertake multigenerational migrations in the Afro-tropical and Palearctic ranges, and understanding their migratory connectivity remains challenging due to their small size, short life span and large population sizes. Hydrogen isotopes (δ2 H) can be used to reconstruct the movement of dispersing or migrating insects, but applying δ2 H for provenance requires a robust isotope baseline map (i.e. isoscape) for the Afro-Palearctic. We analyzed the δ2 H in the wings (δ2 Hwing ) of 142 resident butterflies from 56 sites across the Afro-Palearctic. The δ2 Hwing values were compared to the predicted local growing-season precipitation δ2 H values (δ2 HGSP ) using a linear regression model to develop an insect wing δ2 H isoscape. We used multivariate linear mixed models and high-resolution and time-specific remote sensing climate and environmental data to explore the controls of the residual δ2 Hwing variability. A strong linear relationship was found between δ2 Hwing and δ2 HGSP values (r2 = 0.53). The resulting isoscape showed strong patterns across the Palearctic but limited variation and high uncertainty for the Afro-tropics. Positive residuals of this relationship were correlated with dry conditions for the month preceding sampling whereas negative residuals were correlated with more wet days for the month preceding sampling. High intra-site δ2 Hwing variance was associated with lower relative humidity for the month preceding sampling and higher elevation. The δ2 Hwing isoscape is applicable for tracing herbivorous lepidopteran insects that migrate across the Afro-Palearctic range but has limited geolocation potential in the Afro-tropics. The spatial analysis of uncertainty using high-resolution climatic data demonstrated that many African regions with highly variable evaporation rates and relative humidity have δ2 Hwing values that are less related to δ2 HGSP values. Increasing geolocation precision will require new modeling approaches using more time-specific environmental data and/or independent geolocation tools.

Change Trend and Attribution Analysis of Reference Evapotranspiration under Climate Change in the Northern China

Reference evapotranspiration (ET0), an essential variable used to estimate crop evapotranspiration, is expected to change significantly under climate change. Detecting and attributing the change trend in ET0 to underlying drivers is therefore important to the adoption of agricultural water management under climate change. In this study, we focus on a typical agricultural region of the Fenwei Plain in northern China and use the Mann–Kendall test and contribution rate to detect the change and trend in ET0 at annual and seasonal scales and determine the major contribution factors to ET0 change for the baseline period (1985–2015) and the future period (2030–2060) based on high-resolution gridded data and climatic data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results indicate that the annual ET0 of the Fenwei Plain showed a significant decreasing trend in the baseline period but insignificant and significant increasing trends in the future period under the SSP245 and SSP585 scenarios, respectively. The annual ET0 of the plain under the SSP245 and SSP585 scenarios increase by 4.6% and 3.0%, respectively, compared to the baseline period. The change and trend in ET0 between the four seasons are different in the baseline and future periods. Winter and autumn show clear increases in ET0. VPD is the major contribution factor to the ET0 change in the plain. The change in ET0 is mainly driven by the climatic variables that change the most rather than by the climatic variables that are the most sensitive to the ET0 change. The change and trend in ET0 in the plain showed clear spatial differences, especially between the eastern and western area of the plain. To adapt to the impact of climate change on ET0, the irrigation schedule of the crops cultivated in the plain, the cropping system and management of the irrigation district in the plain need to be adjusted according to the change characteristics of spatial and temporal ET0 in the future. These results contribute to understanding the impacts of climate change on evapotranspiration in the study region and provide spatial and temporal references for adaptation in managing agricultural water use and crop cultivation under climate change.

Reconstruction of Continuous High-Resolution Sea Surface Temperature Data Using Time-Aware Implicit Neural Representation

Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño–Southern Oscillation (ENSO) is considered a significant signal of the global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information (T_INRI) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. Traditional deep learning models for generating high-resolution climate data are only applicable to fixed-resolution enhancement scales. In contrast, the proposed T_INRI method is not limited to the enhancement scale provided during the training process and its results indicate that it can enhance low-resolution input by arbitrary scale. Additionally, we discuss the impact of temporal information on the generation of high-resolution climate data, specifically, the influence of the month from which the low-resolution sea surface temperature data are obtained. Our experimental results indicate that T_INRI is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T_INRI performance for a different calendar month. We also examined the potential capability of T_INRI in recovering missing grid value. These results demonstrate that the proposed T_INRI is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications.

Taking advantage of opportunistically collected historical occurrence data to detect responses to climate change: The case of temperature and Iberian dung beetles.

This study introduces a novel approach to leverage high-resolution historical climate data and opportunistically collected historical species occurrence data for detecting adaptive responses to global change. We applied this procedure to the temperature data and the most comprehensive Iberian dataset of dung beetle occurrences as an illustrative example. To understand how populations of different species are responding, we devised a procedure that compares the temporal trend of spatial and temperature variables at the locations and times of all the occurrence data collection (overall trend) with the specific temporal trends among the occurrences of each species. The prevalence of various species responses is linked to life history or taxonomic characteristics, enabling the identification of key factors influencing the propensity to experience different effects from climate change. Our findings suggest that nearly half of the Iberian dung beetle species may be adversely affected by temperature increases, with a geographic shift being the most common response. The results generated through the proposed methodology should be regarded as preliminary information, serving to formulate hypotheses about the diverse responses of species to climate change and aiding in the selection of candidate species capable of coping with challenges posed by changing temperatures.

Wilderness areas under threat from global redistribution of agriculture

Agriculture expansion is already the primary cause of terrestrial biodiversity loss globally1,2; yet, to meet the demands of growing human populations, production is expected to have to double by 2050.3 The challenge of achieving expansion without further detriment to the environment and biodiversity is huge and potentially compounded by climate change, which may necessitate shifting agriculture zones poleward to regions with more suitable climates,4 threatening species or areas of conservation priority.5,6,7 However, the possible future overlap between agricultural suitability and wilderness areas, increasingly recognized for significant biodiversity, cultural, and climate regulation values, has not yet been examined. Here, using high-resolution climate data, we model global present and future climate suitability for 1,708 crop varieties. We project, over the next 40 years, that 2.7 million km2 of land within wilderness will become newly suitable for agriculture, equivalent to 7% of the total wilderness area outside Antarctica. The increase in potentially cultivable land in wilderness areas is particularly acute at higher latitudes in the northern hemisphere, where 76.3% of newly suitable land is currently wilderness, equivalent to 10.2% of the total wilderness area. Our results highlight an important and previously unidentified possible consequence of the disproportionate warming known to be occurring in high northern latitudes. Because we find that, globally, 72.0% of currently cultivable land is predicted to experience a net loss in total crop diversity, agricultural expansion is a major emerging threat to wilderness. Without protection, the vital integrity of these valuable areas could be irreversibly lost.

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.

Leveraging Deep Learning for Climate Change Prediction Models : A Dive into Cutting-Edge Techniques

In recent years, deep learning has emerged as a promising approach for statistical downscaling, which involves generating high-resolution climate data from coarse low-resolution variables. However, concerns persist regarding the ability of these models to generalize to future climate change scenarios, primarily due to the assumption of stationarity. In this study, we propose the use of deep ensembles as a straightforward method to enhance the uncertainty quantification of statistical downscaling models. By improving the representation of uncertainty, these models offer superior planning capabilities against extreme weather events, which can have significant negative social and economic impacts. Given the absence of observational future data, we rely on pseudo-reality experiments to evaluate the effectiveness of deep ensembles in quantifying the uncertainty of climate change projections. The adoption of deep ensembles facilitates more robust risk assessment, addressing critical needs in various sectors for adapting to climate change challenges.

Warmer and wetter: Outlining climate services for snow-dependent tourism in Norway – The case of Lofoten

Human-induced climate change potentially impacts nature-based activities in Lofoten and may limit the attractiveness of the destination for tourists seeking recreation and adventure in the mountains. As a climate service, we calculated climate indicators relevant to the tourism sector based on the representative concentration pathways RCP4.5 and RCP8.5 until 2060. We used high-resolution gridded climate data and projections to calculate indicators such as changes in the frequency and intensity of consecutive wet days, changes in precipitation type (snow, sleet, rain), changes in the number of skiing days on ungroomed, natural snow, and changes of the monthly 0 °C-isoline. We found a minor, but non-robust increase in the number of consecutive wet days with a precipitation intensity > 8 mm/day, and a clear change in the precipitation regime depending on altitude that leads to more precipitation falling as rain instead of snow. Also, a strong decrease in the number of skiing days is projected by the climate models as the monthly near-surface 0 °C-isoline increases. These are important findings for long-term planning and investments in the tourism sector in Lofoten, especially as tourism growth is considered an important tool for regional economic development. The analytical methods used in this study are transferable to analyses on a regional to national scale. National maps and data material for 11 regions were recently published on https://klimaservicesenter.no/kss/framskr/sno-sludd-regn.

Environmental modulation of plant mycorrhizal traits in the global flora.

Mycorrhizal symbioses are known to strongly influence plant performance, structure plant communities and shape ecosystem dynamics. Plant mycorrhizal traits, such as those characterising mycorrhizal type (arbuscular (AM), ecto-, ericoid or orchid mycorrhiza) and status (obligately (OM), facultatively (FM) or non-mycorrhizal) offer valuable insight into plant belowground functionality. Here, we compile available plant mycorrhizal trait information and global occurrence data ( 100 million records) for 11,770 vascular plant species. Using a plant phylogenetic mega-tree and high-resolution climatic and edaphic data layers, we assess phylogenetic and environmental correlates of plant mycorrhizal traits. We find that plant mycorrhizal type is more phylogenetically conserved than plant mycorrhizal status, while environmental variables (both climatic and edaphic; notably soil texture) explain more variation in mycorrhizal status, especially FM. The previously underestimated role of environmental conditions has far-reaching implications for our understanding of ecosystem functioning under changing climatic and soil conditions.

Forest growth in Europe shows diverging large regional trends

Forests cover about one-third of Europe’s surface and their growth is essential for climate protection through carbon sequestration and many other economic, environmental, and sociocultural ecosystem services. However, reports on how climate change affects forest growth are contradictory, even for same regions. We used 415 unique long-term experiments including 642 plots across Europe covering seven tree species and surveys from 1878 to 2016, and showed that on average forest growth strongly accelerated since the earliest surveys. Based on a subset of 189 plots in Scots pine (the most widespread tree species in Europe) and high-resolution climate data, we identified clear large-regional differences; growth is strongly increasing in Northern Europe and decreasing in the Southwest. A less pronounced increase, which is probably not mainly driven by climate, prevails on large areas of Western, Central and Eastern Europe. The identified regional growth trends suggest adaptive management on regional level for achieving climate-smart forests.

Combining genetic and environmental data to map and model regions of provenance for silver fir (Abies alba Mill.) in Italy

Regions of provenance for forest reproductive materials are the basis for wise use of forest resources in a changing climate. In this work a modelling framework is proposed for silver fir (Abies alba Mill.) in Italy where genetic clusters described by nuclear microsatellites were combined with high-resolution climatic data. When the genetic clusters were too large or had an uncertain ecological niche expression, an additional subregion division-was evaluated according to a climatic assessment. Subsequently each genecological group (Region of Provenance, RoP) was projected in geographic space separately using species distribution modelling (SDM) procedure under current (1991–2020) and a future climate scenario derived from the 6th assessment report for the period 2041–2070. The final division into nine RoPs was able to explain 77.41% of the total climatic variance, a good trade-off between statistical significance and practical usability. The modelling steps then showed a large degree of ecological overlap between RoPs with some of them occurring in similar ecological environments but characterized by a different genetic structure. When projected at the continental scale, the Italian RoPs were found to be suitable for almost all the current European range of silver fir, with potential expansion in Nordic countries in the future, beyond the current distribution range. The study showed that the combination of genetic and ecological data can be a robust way to proceed in areas where a strong genetic differentiation between populations occurs, such as in Italy. New markers such as SNPs can then be used to detect adaptive traits and drive the selection of provenances for common garden experiments in areas where the SDM modelscurrently extrapolate potential sites outside the current natural range.

Altitudinal shifting of major forest tree species in Italian mountains under climate change

Climate change has profound implications for global ecosystems, particularly in mountainous regions where species distribution and composition are highly sensitive to changing environmental conditions. Understanding the potential impacts of climate change on native forest species is crucial for effective conservation and management strategies. Despite numerous studies on climate change impacts, there remains a need to investigate the future dynamics of climate suitability for key native forest species, especially in specific mountainous sections. This study aims to address this knowledge gap by examining the potential shifts in altitudinal range and suitability for forest species in Italy's mountainous regions. By using species distribution models, through MaxEnt we show the divergent impacts among species and scenarios, with most species experiencing a contraction in their altitudinal range of suitability whereas others show the potential to extend beyond the current tree line. The Northern and North-Eastern Apennines exhibit the greatest and most widespread impacts on all species, emphasizing their vulnerability. Our findings highlight the complex and dynamic nature of climate change impacts on forest species in Italy. While most species are projected to experience a contraction in their altitudinal range, the European larch in the Alpine region and the Turkey oak in the Apennines show potential gains and could play significant roles in maintaining wooded populations. The tree line is generally expected to shift upward, impacting the European beech—a keystone species in the Italian mountain environment—negatively in the Alpine arc and Northern Apennines, while showing good future suitability above 1,500 meters in the Central and Southern Apennines. Instead, the Maritime pine emerges as a promising candidate for the future of the Southern Apennines. The projected impacts on mountain biodiversity, particularly in terms of forest population composition, suggest the need for comprehensive conservation and management strategies. The study emphasizes the importance of using high-resolution climate data and considering multiple factors and scenarios when assessing species vulnerability. The findings have implications at the local, regional, and national levels, emphasizing the need for continued efforts in producing reliable datasets and forecasts to inform targeted conservation efforts and adaptive management strategies in the face of climate change.

Cooling Energy Benefits of Increased Green Infrastructure in Subtropical Urban Building Environments

Due to urban warming, the energy demand for cooling buildings is rising. The current study used CitySim (building energy model) to estimate the cooling energy requirements for 40 buildings in downtown Dubai using high-resolution climate data from weather research and forecasting (WRF) coupled with the single layer urban canopy model (SLUCM). Simulating the four mitigation scenarios allowed for the examination of the reduction in cooling load caused by the addition of greenery at a rate ranging from 25% to 100%. The insulated building’s cooling demand reduced by a maximum of 13.89% under 100% GI (M4). Scenario M4 resulted in a reduction of 4.6 kWh/m2 and 3.1 kWh/m2 for the non-insulated and insulated low-rise residential buildings, respectively, while the high-rise buildings saw a reduction of 3.09–4.91 kWh/m2 for the non-insulated and 2.07–3.09 kWh/m2 for the insulated buildings. This study offers a potential remedy to deal with the problem of urban heating in subtropical environments.

Temperature change in the Tianshan Mountains and its external drivers

The Tianshan Mountains area (TMA), located in east Central Asia, has experienced faster temperature rise than the global average rate from the 1960s to the present. This initial estimate is carefully examined using a new dataset of high spatial resolution dynamically downscaled by an updated Weather Research and Forecasting (WRF) regional model. This dataset and other available climate data products show that the WRF model outputs describe temperature changes in TMA consistent with limited available observations (R2 = 0.95 and RMSE = 3.25 °C). The model data confirm that the temperature at TMA has risen at a rate of 0.18 °C/decade, faster than the global mean rate of 0.15 °C/decade. Strong warming rates are also found in spring and summer while the winter is cooling. In addition to revealing these variations of TAM temperatures the model outputs provide high resolution meteorological and climatic data and open the opportunity for studying surface hydrology and ecosystem responses in the region. Possible forcings of the changes of TMA temperature are explored from a global perspective using a set of ten climate indices. Results show that the most impactful processes on TMA temperatures are those associated with the contraction of the arctic sea-ice in recent decades, SST anomalies in the tropical Pacific, e.g., ENSO, SST variations in both the tropical Atlantic and North Atlantic Ocean. The east Asian jet stream also shows a significant impact on TMA temperatures likely by its impact on placing its entrance region over the Central Asia and thus weather disturbances in TMA.

Coupled high-resolution GCM downscaling framework for projecting dynamics and drivers of ecosystem services in Pearl River Basin, China

The projection of future ecosystem service (ES) dynamics under SSP-RCP scenarios would be beneficial for the development of effective climate mitigation strategies for policymakers. However, current projection frameworks generally neglect the downscaling of high-resolution climate and land use data, leading to uncertainties in the simulation of ESs under the SSP-RCP scenarios. To address this issue, this study proposes an integrated analysis framework to downscale climate and land use data. We first identified areas of high risk in terms of inadequate water-related ESs in the Pearl River Basin (PRB) of China. Subsequently, we analyzed key socio-environmental factors driving the dynamics of water-related ESs under future SSP-RCP scenarios. The results indicate that 1) water-related ESs in the PRB are expected to show an overall increasing trend, 2) the sub-watersheds located in the downstream is at a higher risk of experiencing insufficient supply capacity for water-related ESs, and 3) under the coupling effects of climate change, land use change, and socio-economic development, drastic changes in future land use are expected to have a stronger impact on watershed ESs than the climate (i.e., global warming). Our work provides valuable insights for policymakers and managers regarding future water and land resource planning. The proposed framework can be integrated with other hydrological or land surface models to project the spatial–temporal evolution characteristics of future watershed ESs with finer temporal resolution.

Microbial ecological clusters structured by environments drive maize residue decomposition at the continental scale

Environmental factors (e.g., climate and edaphic factors) indirectly regulate residue decomposition via microbial communities. Microbial ecological clusters (eco-clusters) structured by specific environmental factors have consequences for ecosystem functions. However, less is known about how microbial eco-clusters affect residue decomposition, especially over broad geographic scales. We collected agricultural soils from adjacent pairs of upland and paddy fields along a latitudinal gradient from the cold-temperature zone to the tropical zone, and conducted a microcosm experiment with 13C-labelled maize residue to explore the continental pattern of maize residue-derived 13CO2 (RDC), and whether and how microbial eco-clusters drive and predict RDC. Results showed that RDC decreased with latitude in both upland and paddy fields. Further, we identified 21 well-defined eco-clusters according to microbial environmental preferences, which explained 51.15 % of the spatial variations in RDC. The eco-clusters of high-total annual precipitation (TAP), high-mean annual temperature (MAT), low-pH, and some low-nutrient-associated exerted a positive effect on RDC. These eco-clusters contained many taxa belonging to the Actinobacteriota, Firmicutes, and Sordariomycetes, and their relative abundance decreased with latitude. Upland soils displayed 2.40-fold of RDC over paddy soils. Low-pH and high-organic matter (OM) eco-clusters were found to be the most prominent predictors of RDC in upland and paddy fields, respectively. Finally, we constructed a continental atlas of RDC in both upland and paddy fields based on eco-clusters and high-resolution climate and soil data. Overall, our study provides important evidence that historical environment-shaped microbial eco-clusters can drive and predict residue decomposition, providing new insights into how environmental factors indirectly regulate residue decomposition.