Representative Concentration Pathway Scenarios Research Articles

Effects of forest management intensity and climate change severity on volume growth, timber yield, carbon stocks, and the amount of deadwood in Scots pine, Norway spruce and silver birch stands in boreal conditions

We studied how management intensity and climate severity affect volume growth, timber yield, carbon stocks, and the amount of deadwood in Scots pine (Pinus sylvestris (L.), Norway spruce (Picea abies (L.) Karst.) and silver birch (Betula pendula Roth.) dominated stands in the Republic of Karelia and Arkhangelsk region of northwest Russia. Using the forest ecosystem model (SIMA) under different climates (current and representative concentration pathway scenarios, RCP4.5 and RCP8.5), no-thinning, low, medium, and high intensity thinning rotational forestry regimes were simulated. Under RCPs, the volume growth and timber yield (5-53%), carbon stocks (1-22%), and deadwood amounts (11-75%) increased for all Scots pine and silver birch stands. The use of low intensity management increased volume growth and carbon stocks (3-16%) and deadwood amount (up to 60%) under RCPs, but not timber yield (±3%) in these stands. For Norway spruce stands, the volume growth (5-26%), timber yield (23-75%), and carbon stocks (5-15%) decreased under RCP8.5, but deadwood amount increased (up to 142%). Intensive management increased volume growth (4-19%), timber yield (4-63%), carbon stocks (up to 14%) and deadwood amounts (up to 49%). Our results highlight that effects of climate severity and management intensity are site and species-specific for Eurasian’s boreal forests.

Historical and Future global warming under different RCP emission scenarios

Understanding the relationship between carbon emissions and climate change has become increasingly urgent amidst the universal endorsement of sustainable development and the establishment of net-zero carbon emission target (The Paris Agreement). In this study, model projections based on Coupled Model Intercomparison Project Phase 5 (CMIP5) are used to examine future climate change under different representative concentration pathway (RCP) emission scenarios till 2300. Besides analysis of future global average temperature change under the emission scenarios of RCP2.6, RCP4.5, RCP6.0, and RCP 8.5, historical data is first collected during the past three decades (1990-2021). The results show that the simulated global warming ranges from 0.9 to 1.3 between 1990 and 2021, consistent with the observations (0.93). Carbon emissions in China arose mostly from manufacturing and construction, electricity and heat and transport. Emissions from electricity, heat and transport increase by a high ratio of 7.7 and 3.5 times due to industry development. Under different RCP scenarios, the global surface temperature anomaly will increase by 0.2-1.0, 1.2-2.0, 3.5-4.3 and 3.0-8.5 , respectively. The surface temperature anomaly values under RCP8.5 are much higher than those under other emission scenarios. Only in the RCP2.6 scenario (sustainability), the surface warming in all simulations stays below a 2 threshold at the end of the 21st century. To attain net zero emissions, it is highly demanded to develop strict emission policies, chart a course toward decarbonization, and institute effective measures aimed at realizing net zero emissions within the short to medium term. Concurrently striving for economic objectives, managing the equilibrium between economic growth and environmental sustainability emerges as a critical imperative.

Streamflow Variation under Climate Conditions Based on a Soil and Water Assessment Tool Model: A Case Study of the Bailong River Basin

We coupled the global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and Future Land Use Simulation model (FLUS) to evaluate land use change in the Bailong River Basin (BRB) under three shared socioeconomic pathway and representative concentration pathway scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5). Additionally, we used calibrated soil and water assessment tools (SWATs) to evaluate the streamflow in the BRB from 2008 to 2100 under the combined influence of climate and land use changes. The results indicate that (1) under the SSP126-EP scenario, forests have been well preserved, and there has been an increase in the combined area of forests and water bodies. The SSP245-ND scenario has a similar reduction pattern in agricultural land as SSP126-EP, with relatively good grassland preservation and a moderate expansion rate in built-up land. In contrast, the SSP585-EG scenario features a rapid expansion of built-up land, converting a significant amount of farmland and grassland into built-up land. (2) From 2021 to 2100, the annual average flow increases under all three scenarios, and the streamflow change is most significant under SSP5–8.5. (3) Compared to the baseline period, the monthly runoff increases, with the most significant increase occurring during the summer months (June to August). This study offers a thorough assessment of potential future changes in streamflow. Its findings are expected to be applied in the future to improve the management of water resources at a local level.

Temperature-photoperiod interactions improve simulations of early xylem phenology: refining the Vaganov-Shashkin growth model

Wood formation plays a crucial role in forest productivity and carbon sequestration. Changes in early xylem phenology influence wood development (xylogenesis) and tree growth, thereby affecting carbon uptake by forests. However, a deeper understanding of long-term shifts in spring growth phenology in response to climate warming is lacking. Process-based simulation tool models, such as the Vaganov-Shashkin (VS) growth model, could be used to simulate and trace shifts in spring growth phenology. We propose upgrading the VS model with a new function that considers the temperature-photoperiod interaction and improves the simulation of growth onset timings. To assess this refined VS model, we compared the early growing xylogenesis phases of two conifers (Pinus sylvestris and Pinus pinaster) and two ring-porous oaks (Quercus pyrenacia and Quercus faginea) coexisting in a Mediterranean continental site located in Soria, central Spain. The improved VS model successfully simulated early growing season xylogenesis during the warm-dry 2012 year, which can be considered analogous to the forecasted warmer scenarios in the 21st century. The upgraded VS model improved its ability to simulate growth onset, demonstrating the importance of considering both temperature and photoperiod. The model was then used to simulate spring phenology considering two Representative Concentration Pathways scenarios (RCP4.5 and RCP8.5) with different warming rates. An earlier, but species-specific, xylem onset was forecasted for the four tree species in response to warmer spring conditions in the late 21st century.

Analyzing energy consumption due to occupant interaction with manual and automatic window blinds in multiple climates

Proper daylight management through automated or manual blind systems can enhance user comfort and energy conservation. Furthermore, the influence of weather conditions, coupled with occupant behavior, significantly impacts energy consumption, rendering weather forecasts invaluable in conducting individual building energy simulations to identify energy-efficient strategies for the future. Also, I wonder if it is possible to move some items from room 308 until you can provide items from other resources. Future weather conditions and their impact on building performance can be evaluated using Representative Concentration Pathways (RCP) scenarios provided by the Intergovernmental Panel on Climate Change (IPCC). This research aimed to compare the energy usage of manual and automated window blinds with internal blinds in mid-size office buildings across four main climate zones (2B Hot-Dry; 2A Hot-Humid; 7 Very Cold; 4A Mixed-Humid) in the USA under current and future climate change scenarios (RCP 4.5, 8.5). Utilizing simulation as the methodology and comparing the energy use resulting from the RCP 4.5 and 8.5 scenarios revealed that the most significant decrease in heating energy usage was happening in the Hot-Humid zone with Automated blinds, which is a more efficient alternative than manual Blinds. In contrast, the Very Cold zone experienced almost no changes in either heating or cooling energy consumption. In Climate Zones 2B Hot-Dry and 7 Very Cold, rising temperatures lead to increased cooling energy requirements and reduced heating demands due to climate changes. Climate Zone 4A Mixed-Humid saw reduced energy consumption for cooling but increased usage for heating, particularly under RCP 8.5. In Zone 2A Hot-Humid, a high demand for heating energy was found.

China's future wind energy considering air density during climate change

To assess the impact of air density on future wind resources under representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5), the Coordinated Regional Downscaling Experiment-East Asia project was utilized to simulate the future air density characteristics of China (2021–2100). Empirical air density distributions were calculated, and fourteen probability density functions were fitted to the empirical air density distributions, determining the optimal fitted distribution functions. In this study, the effect of air density on the annual energy production and wind power density was quantified. The results indicated that only approximately 4 % of regional air density values reaches the standard air density under the three climate scenarios. The seven parameter Generalized Extreme Value-Generalized Extreme Value distribution, six parameter Gamma-Generalized Extreme Value distribution, and six parameter Exponentiated Weibull- Generalized Extreme Value distribution matched the empirical distributions well under RCP2.6, RCP4.5, and RCP8.5, respectively. Annual energy production and wind power density decreased by more than 15 % after considering air density. Under the different RCP scenarios (RCP2.6, RCP4.5, and RCP8.5), the errors of annual energy production and wind power density gradually escalate. In the Tibetan Plateau region, accounting for air density results in an overestimation of annual energy production by over 40 % and wind power density by more than 35 %. The findings of this study offer guidance for future wind energy resource planning, enabling optimal utilization of wind energy resources.

Influence of Regional Temperature Anomalies on Strawberry Yield: A Study Using Multivariate Copula Analysis

A thorough understanding of the impact of climatic factors on agricultural production is crucial for improving crop models and enhancing predictability of crop prices and yields. Fluctuations in crop yield and price can have significant implications for the market sector and farming community. Given the projected increase in frequency and intensity of extreme events, reliable modelling of cropping patterns becomes essential. Temperature anomalies are expected to play a prominent role in future extreme events, emphasizing the need to comprehend their influence on crop yield. Forecasting extreme yield, which encompasses both the highest and lowest levels of agricultural production within a given time period, along with peak crop prices representing the highest market values, poses greater challenges in forecasting compared to other values. Probability-based predictions, accounting for uncertainty and variability, offer a more accurate approach for extreme value estimation and risk assessment. In this study, we employ a multivariate analysis based on vine copula to explore the interdependencies between temperature anomalies and daily strawberry yield in Santa Maria, California. By considering the maximum and minimum daily yields each month, we observe an increased probability of yield loss with rising temperature anomalies. While we do not explicitly consider the specific impacts of temperature anomalies under individual Representative Concentration Pathway (RCP) scenarios, our analysis is conducted within the broader context of the current global warming scenario. This allows us to capture the overall anticipated effects of regional temperature anomalies on agriculture. The findings of this study have potential impacts and consequences for understanding the vulnerability of agricultural systems and improving crop model predictions. By enhancing our understanding of the relationships between temperature anomalies and crop yield, we can inform decision-making processes related to the impact of climate change on agriculture. This research contributes to the ongoing efforts in improving agricultural sustainability and resilience in the face of changing climatic conditions.

A machine learning approach to mapping suitable areas for forest vegetation in the eThekwini municipality

Driven by climate change, global forests are undergoing significant transformations in growth, ecology, and distribution, necessitating informed restoration and conservation strategies, particularly in the eThekwini Municipality where anthropogenic activities exacerbate these trends. Modelling current forest suitability (2023) utilized bioclimatic variables from the WorldClim dataset, alongside elevation and slope from the Shuttle Radar Topography Mission (SRTM) dataset, with remote sensing data acquired from Landsat 9 and Sentinel 2A. Future forest suitability (2021–2040) was projected also using bioclimatic variables from two Global Climate Models (GCMs) under four WorldClim Shared Socioeconomic Pathway (SSP)-based Representative Concentration Pathway (RCP) scenarios. Employing Random Forests (RF), Light Gradient Boosting (LightGBM), and Artificial Neural Networks (ANN), data processing was carried out using Google Earth Engine (GEE), QGIS and Python, with model accuracy primarily assessed using the Receiver Operating Characteristic (ROC) curves and the Area Under the ROC Curve (AUC). LightGBM demonstrated superior performance, achieving AUCs of 96.88% and 93.75% for current and future suitability mapping, respectively, with annual precipitation and vegetation changes identified as crucial variables. Currently, 30% of the municipality's land is deemed suitable, primarily concentrated in the central region. Future projections highlight the mountainous north-western region as most suitable, notably under the SSP370 scenario with a projected suitable area of 63%. Strategic recommendations include prioritizing reforestation efforts, engaging private landowners, exploring urban reforestation opportunities, and implementing continuous monitoring for adaptive management, thereby enhancing carbon sequestration, biodiversity conservation, and ecosystem resilience. This study provides valuable insights for informed decision-making in forest restoration and conservation, despite inherent uncertainties.

Forecasting water scarcity with an optimized Soil and Water Assessment Tool (SWAT) model under representative concentration pathway (RCP) scenarios

Abstract Population growth and climate change have increased the demand for water resources, and there is a need to predict water scarcity effectively. The soil and water assessment tool model has been optimized in this work to estimate water scarcity under several representative concentration pathway scenarios. To improve the Soil and Water Assessment Tool model, a new version of the dwarf mongoose optimization technique named Improved Dwarf Mongoose Optimization has been developed. The findings showed that the optimized Soil and Water Assessment Tool model performs better than other optimization models in terms of water scarcity prediction accuracy. The findings indicated that the improved Soil and Water Assessment Tool model can be used to predict water scarcity in other locations and provide valuable insights into the future of water resource management. According to the results, water availability also decreased in the representative concentration pathways, but the trend declined more in the representative concentration pathways 8.5 scenarios. Critical times of water scarcity have been discovered that can guide water management techniques. The results of this study can help policymakers make informed choices about water management and develop adaptive methods to mitigate the effects of water scarcity.

Possible changes and occurrences of future wave power in an enclosed sea using a high-resolution regional climate model

The present study evaluates future wave power resources and detects a possible impact of climate change on wave power in an enclosed sea (Black Sea) for the 21st century. The wave data are obtained from a third-generation spectral wave model using a validated RCM (Regional Climate Model). The wave power projection and projected occurrences are analyzed for near and middle future (NF, MF) periods based on two RCP (Representative Concentration Pathway) scenarios (4.5 and 8.5). Possible changes in wave power for the NF 2021–2060 and MF 2061−2100 periods are evaluated by comparing the historical (1970–2005) period. According to two RCP scenarios, the wave power potential for the NF is expected to increase markedly over the entire basin, while, for the MF, the changes are projected by decreases, especially in the northeastern and western parts of the basin. Furthermore, projected occurrences are examined using Energy Level Occurrences known as available, moderate, and rich level occurrences. The 15 reference points over the basin are chosen to detect the most suitable location(s) for future wave power developments. All spatial and local analyses indicate that the western basin, especially the southwest, will continue having high wave power potential and occurrences in future periods.

The Impact of Climate Change on Hydro-Meteorological Droughts in the Chao Phraya River Basin, Thailand

This study evaluated the impacts of climate change on hydro-meteorological droughts in the Chao Phraya River Basin (CPRB), Thailand under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). We used three Reginal Climate Models (RCMs) of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment—Southeast Asia (SEACLID/CORDEX-SEA), which are bias corrected. The Soil and Water Assessment Tool (SWAT) was used to simulate streamflow for future periods. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were estimated and used for drought characterization at three time scales (3, 6, and 12 months). The lag time between meteorological and hydrological droughts is approximately 1–3 months. The results suggest that the CPRB is likely to experience less frequent hydro-meteorological drought events in the future. The meteorological drought is projected to be longer, more severe, and intense. The severity of hydrological drought tends to decrease, but the intensity could increase. Climate change has been discovered to alter drought behaviors in the CPRB, posing a threat to drought monitoring and warning because droughts will be less predictable in future climate scenarios. The characterization of historical and future droughts over the CPRB is therefore valuable in developing an improved understanding of the risks of drought.

Assessing the impacts of climate change on crop yields, soil organic carbon sequestration and N2O emissions in wheat–maize rotation systems

Straw retention has been widely implemented to increase soil organic carbon (SOC) sequestration and greenhouse gas (GHG) mitigation for global agriculture. However, the combined effects of long-term straw retention on crop production, SOC sequestration and GHG emissions response to climate change remain unknown. Two nearby wheat–maize rotation field experiments in the North China Plain, combined with local weather, soil and agronomic measurements, were used to evaluate the applicability of the SPACSYS model. The model was then applied to assess the response of crop yield, SOC storage and nitrous oxide (N2O) emissions to three nitrogen fertilizer application levels (100, 200, 400 kg N ha−1) and three representative concentration pathway scenarios (RCP2.6, RCP4.5 and RCP8.5) under straw retention for the wheat–maize rotation systems during 2021–2100. In general, the model was reasonably accurate with R2 and model efficiency (EF) ranging from 0.25 to 0.96 and 0.32–0.94, respectively. Climate change decreased wheat yield by 4–39%, while maize showed a slight increase (3%) under RCP2.6 and a decrease of 1–15% under RCP4.5 and RCP8.5. The SOC storage in the 0–20 cm soil increased at a rate of 73–195 kg C ha−1 yr−1 but decreased within the upper 1 m soil at 280–390 kg C ha−1 yr−1. Climate change reduced the positive effect of SOC sequestration except in RCP2.6 and stimulated substantial N2O emissions ranging from 1–7.5 to 2.9–23.2 kg N ha−1 yr−1, consequently, the global warming potential increased from –79–2555 to 548–9357 kg CO2-eq ha−1 yr−1 under various N fertilizer application levels. This study reveals that the negative feedback under climate change with modelling approach, and extensive efforts are needed to make adaptations to ensure food production and reduce GHG emissions in the future.

Projected Heat Waves in Ecuador under Climate Change: Insights from HadGEM-RegCM4 Coupled Model

This study examines heat wave projections across Ecuador’s Coastal, Highlands, and Amazon regions for 1975–2004 and 2070–2099 under Representative Concentration Pathways (RCP) scenarios 2.6, 4.5, and 8.5. Employing dynamic downscaling, we identify significant increases in heatwave intensity and maximum air temperatures (Tmax), particularly under RCP 8.5, with the Coastal region facing the most severe impacts. A moderate positive correlation between Tmax and climate indices such as the Pacific Decadal Oscillation (PDO) and the Oceanic Niño Index (ONI) suggests regional climatic influences on heatwave trends. These findings highlight the critical need for integrated climate adaptation strategies in Ecuador, focusing on mitigating risks to health, agriculture, and ecosystems. Proposed measures include urban forestry initiatives and the promotion of cool surfaces, alongside enhancing public awareness and access to cooling resources. This research contributes to the understanding of climate change impacts in Latin America, underscoring the urgency of adopting targeted adaptation and resilience strategies against urban heat island effects in Ecuador’s urban centers.

Effects of grazing and climate change on aboveground standing biomass and sheep live weight changes in the desert steppe in Inner Mongolia, China

CONTEXTThe Stipa breviflora desert steppe ecosystem is fragile and sensitive to climate change and grazing disturbance. Previous studies have reported the effects of climate change and grazing on aboveground standing biomass of the plant community and sheep live weight, however, the interaction between climate change and grazing remains unclear. Process models have become ideal tools for the evaluation of the effects of grazing management practices under climate change. OBJECTIVEWe used the Soil-Plant-Atmosphere Continuum System (SPACSYS) model to investigate aboveground standing biomass and the live weight of sheep in the desert steppe of Inner Mongolia under future climate change scenarios and different grazing management. The results will be used to inform adaptive management strategies. METHODSThe grazing experiment consisted of four treatments: no grazing (0 sheep ha−1 half year−1), light stocking rate (0.91 sheep ha−1 half year−1), moderate stocking rate (1.82 sheep ha−1 half year−1), and high stocking rate (2.71 sheep ha−1 half year−1). We used observed data on soil temperature, soil volumetric water content, changes to sheep live weight, and aboveground standing biomass of plant community to provide parameterization and validation for the SPACSYS model. We then predicted aboveground standing biomass of the plant community and sheep live weight changes for different grazing management under three representative concentration pathways (RCP) scenarios (RCP 2.6, RCP 4.5, and RCP 8.5) from 2021 to 2100. RESULTS AND CONCLUSIONSModerate and high stocking rates decreased aboveground standing biomass and sheep live weight changes more than the light stocking rate. A light stocking rate can maintain higher aboveground standing biomass and sheep live weight as well as meet production requirements. Therefore, a light stocking rate is a potentially effective management approach to improve food production security and combat global climate change in the desert steppe. SIGNIFICANCEThe model can inform management strategies for grazing in the desert steppe under climate change, supporting efforts to maintain the stability of the steppe ecosystem and increase economic benefits, while also providing a theoretical basis for adaptive management in the desert steppe.

Climate change influences on the streamflow and sediment supply to the Chao Phraya River basin, Thailand

This study investigates the effects of climate change on the sediment loads of the Ping and Wang River basins and their contribution to the sediment dynamics of the lower Chao Phraya River basin in Thailand. The various climate models under different Representative Concentration Pathways (RCPs) scenarios are employed to project sediment loads in future. The findings indicate a significant increase in river flow approximately 20% in the Ping River (PR) and 35% in the Wang River (WR) by the mid-21st century and continuing into the distant future. Consequently, this is expected to result in sediment loads up to 0.33 × 106 t/y in the PR and 0.28 × 106 t/y in the WR. This escalation is particularly notable under the RCP 8.5 scenario, which assumes higher greenhouse gas emissions. Additionally, the research provides insights into the potential positive implications for the Chao Phraya Delta's coastal management. Without further damming in the Ping and Wang River basins, the anticipated rise in sediment supply could aid in mitigating the adverse effects of land subsidence and sea-level rise, which have historically caused extensive shoreline retreat in the delta region, particularly around Bangkok Metropolis. The paper concludes that proactive adaptation strategies are required to manage the expected changes in the hydrological and sediment regimes to protect vulnerable coastal zones and ensure the sustainable management of the Chao Phraya River Basin in the face of climate change.

Response of climate change and land use land cover change on catchment-scale water balance components: a multi-site calibration approach

Abstract The present study focused on evaluating the separate and combined response of land use land cover and climate change (CC) on future water balance components of a Subarnarekha River basin, spanning between the latitudes 21°33′N–23°18′N and longitudes 85°11′E–87°23′E, situated in the eastern India. The Soil and Water Assessment Tool is used for single-site calibration and multi-site calibration (MSC) of the model to characterize the future water balance components of the basin using the Cellular Automata-Markov model and climate projections under two representative concentration pathway (RCP) scenarios (4.5 and 8.5). The findings indicate that the model parameters obtained through MSC better represent spatial heterogeneity, making it the preferred calibration approach for model simulations. In the middle region of the basin, future annual water yield, groundwater recharge (GWR), and streamflow showed a reduction, respectively, by 46–47%, 29–30%, and 13–15%, while evapotranspiration showed an increase by 5–7% following projected CC under both RCP scenarios. The findings are relevant for policy-makers to mitigate the adverse effects of reduced GWR for sustainable water resources management. Future research may integrate reservoir operation frameworks to effectively address the water management issues of the basin.

Widespread increase in plant transpiration driven by global greening

Global plant transpiration (PT) is a crucial component of the Earth's hydrological cycle and plays a significant role in regulating the exchange of water and energy between the land surface and the atmosphere. However, the long-term trend and the underlying driver of global PT remain unclear due to the significant uncertainties in estimating PT on a global scale. This study uses two sub-Mixture Density Networks (MDNc and MDNa) to predict vegetation canopy resistance (rc) and aerodynamic resistance (ra), then the predicted rc and ra are imported into the Penman-Monteith-Leuning (PML) model to simulate PT. The observed PT at 112 SAPFLUXNET sites are used to validate the performance of hybrid MDN-PML model. The verified MDN-PML model is further applied to map the spatial distribution of global PT and reconstruct a long-term (1990–2020) global PT dataset. The results indicate that the long-term average global PT is 397.2 ± 63.1 mm. During the period 1990–2020, the global PT exhibit a significant upward trend (0.79 ± 0.28 mm/year (P < 0.05)), which equates to a 6.0% increase compared with the long-term average global PT. A widespread trend of elevated PT is observed in approximately 70% of the global land surface. The trend attribution analysis results show that the change in leaf area index (LAI) can explain 66.2% of the global PT trend, indicating that elevated LAI due to global greening is the dominant factor contributing to the upward trend in global PT. The elevated LAI can be largely attributed to the CO2 fertilization effect induced by elevated atmospheric CO2 concentration. Additional analysis reveals that the increased global PT is more sensitive to CO2 fertilization effect in high LAI areas than in low LAI areas. Projected climate scenarios indicate that global land surface PT will continue to rise from 2023 to 2100, and the rate of increase in the future will be higher than in historical periods. The rising rates of global PT under the three Representative Concentration Pathway scenarios (RCP) 2.6, RCP4.5, and RCP8.5 climate change scenarios are 0.86 mm/year, 1.16 mm/year, and 1.45 mm/year, respectively, during the period 2023–2100. Our results highlight the impact of global change and vegetation greening on the global PT and hydrological cycle. This study is of great significance for the scientific response to the challenges of climate change for regional water resource management.

Assessing rainfall erosivity changes over China through a Bayesian averaged ensemble of high-resolution climate models

Spatiotemporal variation in rainfall erosivity resulting from changes in rainfall characteristics due to climate change has implications for soil erosion in developing countries. To promote soil and water conservation planning, it is essential to understand past and future changes in rainfall erosivity and their implications on a national scale. In this study, we present an approach that uses a Bayesian model averaging (BMA) method to merge multiple regional climate models (RCMs), thereby improving the reliability of climate-induced rainfall erosivity projections. Our multi-climate model and multi-emission scenario approach utilize five RCMs and two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios for the baseline period (1986–2005) and future periods (2071–2090) to characterize the spatiotemporal projection of rainfall erosivity and assess variations in China. Our results indicate that the two models outperform other models in reproducing the spatial distribution and annual cycle of rainfall erosivity in China. Moreover, we found an increasing trend in the annual rainfall erosivity from the baseline climate up to the RCMs for all models, with an average change in erosivity of approximately 10.9% and 14.6% under RCP4.5 and RCP8.5, respectively. Our BMA results showed an increase in the absolute value of rainfall erosivity by 463.3 and 677.0 MJ·mm·hm−2·h−1, respectively, in the South China red soil region and the Southwest China karst region under the RCP8.5 scenario. This increase indicates that climate warming will significantly enhance the potential erosion capacity of rainfall in these regions. Additionally, our study revealed that the Southwest China karst region and the Northwest China Loess Plateau region are more sensitive to radiation forcing. To mitigate the risk of soil erosion caused by climate change, it is necessary to consider changes in rainfall erosivity, local soil conditions, vegetation coverage, and other factors in different regions and take appropriate soil and water conservation measures.

Spatiotemporal characteristics and variability in the thermal state of permafrost on the Qinghai–Tibet Plateau

AbstractPermafrost degradation on the Qinghai–Tibet Plateau (QTP) has significant impacts on climate, hydrology, and engineering and environmental systems. To understand the temporal and spatial characteristics of permafrost on the QTP, we quantified the variation in active layer thickness (ALT), permafrost thermal state, and future permafrost change under different scenarios using observational data, reanalysis data, and the numerical permafrost model. Generally, ALT ranged from 0.5 to 6.0 m with an average of 2.39 m, and mean annual ground temperature (at a depth of zero annual amplitude for ground temperature) mainly ranged between 0 and −3°C with an average of −0.85°C. The soil temperatures in different layers based on the ERA5‐Land data revealed even stronger increasing trends, for example, 0.245, 0.245, 0.244, and 0.238°C/decade at depths of 0–7, 7–28, 28–100, and 100–289 cm from 1980 to 2021, compared to those during the period from 1960 to 2021, which were 0.153, 0.156, 0.155, and 0.149°C/decade, respectively. The average warming trends in annual mean soil temperature were 0.153 and 0.243°C/decade from 1960 to 2021 and 1980 to 2021, respectively. The average rate of thickening of the ALT among the 10 active layer observation sites was 2.84 cm/year. There was a significant warming trend in ground temperature above ~15 m with warming of 0.063 to 0.120, 0.026 to 0.182, 0.101 to 0.314, and 0.189 to 0.303°C/decade at the QTB01, QTB06, QTB08, and XDTGT sites, respectively, and yearly minimum ground temperatures exhibited stronger warming trends than maximum ground temperatures. In addition, the simulation revealed significant increases in ground temperature at the Xidatan (XDT) and Tanggula (TGL) sites under both historical and future Representative Concentration Pathway (RCP) scenarios, but the increases in ground temperature were significantly greater at TGL than XDT. These findings provide important information for understanding the variability in permafrost degradation processes and improving simulations of permafrost change under climate change on the QTP.

Reproducible WiSDM: a workflow for reproducible invasive alien species risk maps under climate change scenarios using standardized open data

IntroductionSpecies distribution models (SDMs) are often used to produce risk maps to guide conservation management and decision-making with regard to invasive alien species (IAS). However, gathering and harmonizing the required species occurrence and other spatial data, as well as identifying and coding a robust modeling framework for reproducible SDMs, requires expertise in both ecological data science and statistics.MethodsWe developed WiSDM, a semi-automated workflow to democratize the creation of open, reproducible, transparent, invasive alien species risk maps. To facilitate the production of IAS risk maps using WiSDM, we harmonized and openly published climate and land cover data to a 1 km2 resolution with coverage for Europe. Our workflow mitigates spatial sampling bias, identifies highly correlated predictors, creates ensemble models to predict risk, and quantifies spatial autocorrelation. In addition, we present a novel application for assessing the transferability of the model by quantifying and visualizing the confidence of its predictions. All modeling steps, parameters, evaluation statistics, and other outputs are also automatically generated and are saved in a R markdown notebook file.ResultsOur workflow requires minimal input from the user to generate reproducible maps at 1 km2 resolution for standard Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emission representative concentration pathway (RCP) scenarios. The confidence associated with the predicted risk for each 1km2 pixel is also mapped, enabling the intuitive visualization and understanding of how the confidence of the model varies across space and RCP scenarios.DiscussionOur workflow can readily be applied by end users with a basic knowledge of R, does not require expertise in species distribution modeling, and only requires an understanding of the ecological theory underlying species distributions. The risk maps generated by our repeatable workflow can be used to support IAS risk assessment and surveillance.