Combined Effects Of Climate Change Research Articles

Assessing the Spatiotemporal Variations in the Hydrological Response of the Qin River Basin in Loess Plateau, China

In recent decades, the hydrological processes on the Loess Plateau have undergone significant changes due to the combined effects of climate change and human activities. This study investigates the Qin River basin, the core area of the Loess Plateau. Between 1971 and 2000, the hydrometeorology of the flood season in the Qin basin on the Loess Plateau in China changed abruptly in 1982 and 1992. This study uses distributed hydrologic modeling and statistical analysis to distinguish the spatial and temporal impacts of climate change and human activities. The fastest dynamic changes in land use occurred in grassland and construction land, accounting for 0.36% and −0.1%, respectively, from 1982 to 1991. From 1992 to 2000, it was 0.29% and 0.3%, respectively. The increase in basin area where the Normalized Difference Vegetation Index increased was 95%. However, except for significant changes in runoff from 1981 to 1992, there were no significant changes in precipitation, runoff, and temperature during other flood seasons. During the two studied variation periods, from 1982 to 1991 and from 1992 to 2000, the contribution rate of climate change to flood season runoff (CClimate) decreased from 83.26% to 74.47%, while the contribution rate of human activity disturbance to flood season runoff (Chuman) increased from 16.74% to 25.53%. In terms of spatial distribution, from 1982 to 1991, areas with a Chuman > 50% were primarily concentrated in the upper reaches of the basin, while from 1992–2000, these areas shifted to the lower reaches. This study offers valuable insights for water resource planning and soil conservation measures in the Loess Plateau and similar arid regions worldwide, aiming to ensure water resource safety and sustainability in response to climate change and human interference.

Dynamic Data-driven Research on Forest Fire Behavior Overview

In recent years, under the combined effects of climate change and El Niño, extreme weather has been occurring frequently, and natural disasters of all kinds have been increasing. Forest fire is the first of the three major natural disasters in forests, to reduce the possibility of starting forest fires, reduce the damage caused by forest fire disasters, as well as to predict the development trend of forest fires and fight them scientifically, we should systematically and scientifically grasp the law of forest fire occurrence and development. This paper analyses the various influencing factors affecting the behavior of forest fires and classifies meteorological factors and the moisture content of combustible materials as dynamic factors, and topographical factors and other factors of combustible materials except moisture content as slow-change factors. The research on dynamic data-driven forest fire behavior in recent years is reviewed, the advantages of dynamic data-driven technology for simulating fires are analyzed, the constraints of the technology are indicated, and the future development trend is shown.

Changes in Vegetation NDVI and Its Response to Climate Change and Human Activities in the Ferghana Basin from 1982 to 2015

Exploring the evolution of vegetation cover and its drivers in the Ferghana Basin helps to understand the current ecological status of the Ferghana Basin and to analyze the vegetation changes and drivers, with a view to providing a scientific basis for regional ecological and environmental management and planning. Based on GIMMS NDVI3g and meteorological data, the spatial and temporal evolution characteristics of NDVI were analyzed from multiple perspectives with the help of linear trend and Mann–Kendall (MK) test methods using arcgis and the R language spatial analysis module, combined with partial correlation coefficients and residual analysis methods to analyze the impacts of climate change and human activities on the regional vegetation cover of the Ferghana Basin from 1982 to 2015. NDVI driving forces. The results showed the following: (1) The growing season of vegetation NDVI in the Ferghana Basin showed an increasing trend in the 34-year period, with an increase rate of 0.0044/10a, and the spatial distribution was significantly different, which was high in the central part of the country and low in the northern and southern parts of the country. (2) Temperature and precipitation simultaneously co-influenced the vegetation NDVI growth season, with most of the temperature and precipitation contributing in the spring, most of the temperature in the summer being negatively phased and the precipitation positively correlated, and most of the temperature and precipitation in the fall inhibiting vegetation NDVI growth. (3) The combined effect of climate change and human activities was the main reason for the overall rapid increase and great spatial variations in vegetation NDVI in China, and the spatial distribution of drivers, namely human activities and climate change, contributed 44.6% to vegetation NDVI in the growing season. The contribution of climate change and human activities to vegetation NDVI in the Ferghana Basin was 62.32% and 93.29%, respectively. The study suggests that more attention should be paid to the role of human activities and climate change in vegetation restoration to inform ecosystem management and green development.

Human activities caused lake ecological transitions in the Chinese Loess Plateau over the past 1400 years

The combined effects of climate change and human disturbance has degraded lake ecosystems worldwide in recent times, but there are few studies that explore the relative impact of these factors over long timescales. In this paper, we use sedimentary pigment data to reveal variations in algal abundance and the lake ecological environment of a typical subalpine lake (Beilianchi Lake) in the southwestern Chinese Loess Plateau, to distinguish the relative effects of natural climate change and human disturbance over the past 5000 years. Our data show that between 5000 and 1400 cal yr BP, algal abundance exhibited a slow decline without obvious fluctuations, mainly driven by a gradually drying and cooling climate. After 1400 cal yr BP, algal abundance showed large and rapid fluctuations characterized by a pattern of a decline, then increase, and then decline again. During this period, human activities began to affect lake ecology and gradually overshadowed the role of climatic changes as the main driving factor. Enhanced human disturbances caused a rapid and synchronous decline in algal abundance due to increases in soil erosion and reductions in water transparency. Algal biomass levels recovered when the soil erosion intensity decreased during 800–500 cal yr BP corresponding to the period from Southern Song to Yuan Dynasties. Thereafter (during the period of Ming and Qing Dynasties), as human activities became further enhanced and the soil erosion intensity became very high, the lake ecosystem rapidly transitioned to a stable state of low algal growth. This indicates that the lake ecosystem may have had the capacity to recover from moderate external disturbances in the early stages of human activity, but the resilience of the lake ecosystem began to decrease under stronger human disturbances. Our findings provide important empirical evidence for evaluating the current state of lakes and for sound lake management in the future.

Climate drivers and human impacts shape 35‐year trends of coastal wetland health and composition in an urban region

AbstractThe future of coastal wetlands will depend on the combined effects of climate change and human impacts from urbanization and coastal management. Disentangling the effects of these factors is difficult, but satellite imagery archives provide a way to track biological and physical changes in wetlands over recent decades to reveal how coastal wetlands have been changing in response to climate and human drivers. In this study, we used Landsat to monitor the conditions of 32 coastal wetlands in southern California from 1984 to 2019 and identify environmental and human drivers of these trends. Wetland conditions were characterized by vegetation greenness, using the normalized difference vegetation index (NDVI), and by habitat composition, derived from areal estimates of wetland and subtidal habitats. Overall, wetlands displayed three types of long‐term response: greening and gaining wetland (10), greening and losing wetland (16), and browning and losing wetland (6). Regional environmental drivers with overall positive effects on wetland NDVI were sea level, wave height, and precipitation, whereas stream discharge, vapor pressure deficit, and air temperature had negative or nonlinear effects. Wetland area change was primarily correlated with sea level, but response was highly contextual among sites. Negative trends in wetland NDVI and area were more common in larger sites with low elevations and in sites with open inlets. Restoration had mixed effects, with only half of the restored sites showing positive changes in NDVI and wetland area post‐restoration. The important work of managing and restoring urban coastal wetlands is complicated by variability and context and requires us to account for the influence of humans and climate as we build a regional understanding of historic, present, and future wetland health.

Transforming desertification patterns in Asia: Evaluating trends, drivers, and climate change impacts from 1990 to 2022

Owing to the combined effects of climate change and human activities, the type of desertification in the arid regions of Asia has undergone profound changes, and the problem of desertification has become increasingly prominent. To help address this problem, this study clarifies the transformation trends and characteristics of desertification types across Asia and discusses key areas for desertification prevention and control. Based on a series of Landsat satellite images, combined with the classification method of the desertification degree and a machine learning model, the desertification types, trends, and conversion degrees in the arid regions of Asia were systematically evaluated. In addition, the impact mechanism of climate change on the conversion between desertification types was evaluated. The primary results were as follows: (1) Various desertification types in the arid areas of Asia from 1990 to 2022 exhibited evident time series characteristics. Extremely severe desertification expanded from 1990 to 2009 and subsequently shrunk; (2) The degree of desertification in the arid regions of Asia exhibited different change characteristics in different periods, and the converted area of the same conversion type was different in different periods; (3) Different desertification conversion types in different periods have different drivers; however, overall, soil moisture has the greatest contribution to desertification conversion type. These results provide a scientific reference for the formulation of reasonable desertification prevention and control policies.

Exploring urban land surface temperature using spatial modelling techniques: a case study of Addis Ababa city, Ethiopia

Urban areas worldwide are experiencing escalating temperatures due to the combined effects of climate change and urbanization, leading to a phenomenon known as urban overheating. Understanding the spatial distribution of land surface temperature (LST) and its driving factors is crucial for mitigation and adaptation of urban overheating. So far, there has been an absence of investigations into spatiotemporal patterns and explanatory factors of LST in the city of Addis Ababa. The study aims to determine the spatial patterns of land surface temperature, analyze how the relationships between LST and its factors vary across space, and compare the effectiveness of using ordinary least squares and geographically weighted regression to model these connections. The findings showed that the spatial patterns of LST show statistically significant hot spot zones in the north-central parts of the study area (Moran’s I = 0.172). The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic.The result revealed non-stationarity (p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic. The result revealed non-stationarity ((p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. Consequently, Localized understanding of the spatial patterns and the driving factors of LST has been formulated.

Multi‐Decadal Coastal Acidification in the Northern Gulf of Mexico Driven by Climate Change and Eutrophication

AbstractCoastal waters often experience enhanced ocean acidification due to the combined effects of climate change and regional biological and anthropogenic activities. Through reconstructing summertime bottom pH in the northern Gulf of Mexico from 1986 to 2019, we demonstrated that eutrophication‐fueled respiration dominated bottom pH changes on intra‐seasonal and interannual timescales, resulting in recurring acidification coinciding with hypoxia. However, the multi‐decadal acidification trend was principally driven by rising atmospheric CO2 and ocean warming, with more acidified and less buffered hypoxic waters exhibiting a higher rate of pH decline (−0.0023 yr−1) compared to non‐hypoxic waters (−0.014 yr−1). The cumulative effect of climate‐driven decrease in pH baseline is projected to become more significant over time, while the potential eutrophication‐induced seasonal exacerbation of acidification may lessen with decreasing oxygen availability resulting from ocean warming. Mitigating coastal acidification requires both global reduction in CO2 emissions and regional management of riverine nutrient loads.

Vegetation Greening and Its Response to a Warmer and Wetter Climate in the Yellow River Basin from 2000 to 2020

Vegetation greening is time-dependent and region-specific. The uncertainty of vegetation greening under global warming has been highlighted. Thus, it is crucial to investigate vegetation greening and its response to climate change at the regional scale. The Yellow River Basin (YRB) is a vital ecological barrier in China with high ecological vulnerability and climatic sensitivity. The relationship between vegetation greening and climate change in the YRB and the relative contribution of climate change remain to be explored. Using the Enhanced Vegetation Index (EVI) and meteorological observation data, the spatiotemporal patterns of vegetation greening across the YRB in response to climate change at the basin and vegetation sub-regional scales from 2000 to 2020 were analyzed. The impact of human activities on regional greening was further quantified. Results showed that approximately 92% of the basin had experienced greening, at average annual and growing season rates of 0.0024 and 0.0034 year–1, respectively. Greening was particularly prominent in the central and eastern YRB. Browning was more prevalent in urban areas with a high intensity of human activities, occupying less than 6.3% of the total basin, but this proportion increased significantly at seasonal scales, especially in spring. Regional greening was positively correlated with the overall warmer and wetter climate, and the partial correlation coefficients between EVI and precipitation were higher than those between EVI and temperature. However, this response varied among different seasonal scales and vegetation sub-regions. The combined effects of climate change and human activities were conducive to vegetation greening in 84.5% of the YRB during the growing season, while human activities had a stronger impact than climate change. The relative contributions of human activities to greening and browning were 65.15% and 70.30%, respectively, mainly due to the promotion of ecological rehabilitation programs and the inhibition of urbanization and construction projects.

Analysis of Linkage between Long-Term Morphological Spatial Pattern Analysis and Vegetation Carbon Storage of Forests in Hunan, China

The carbon sequestration of forest ecosystems plays a pivotal role in constraining global warming and mitigating climate change. The landscape pattern of forests is being altered due to the combined effects of climate change and human interference. Furthermore, the relationship between forest pattern changes and carbon storage distribution in a long time series remains unclear. Therefore, it is necessary to examine the relationship between forest patterns and carbon density, investigating the variations and similarities in the changes in carbon density across different modes of pattern change over time, and suggestions for forest planning were provided from a perspective focused on pattern change to enhance carbon storage. The Google Earth Engine (GEE) platform’s random forest model was used to map the spatial distribution of forests in Hunan Province for 1996 and 2020, followed by analyzing the correlation between the changes in forest patterns using the morphological spatial pattern analysis (MSPA) and carbon density simulated by the model. Results show that the net growth rate ((area in 2020-area in 1996)/area in 2020) of the forest in Hunan increased 26.76% between 1996 and 2020. The importance scores for the decade average temperature, short-wave length infrared band 1 (SWIR-1), and slope were the highest metrics in the model of carbon density, and were 0.127, 0.107 and 0.089, respectively. The vegetation carbon storage in Hunan Province increased by 31.02 Tg, from 545.91 Tg to 576.93 Tg in 25 years. This study demonstrates that vegetation carbon storage is influenced by the pattern type in both newly established and pre-existing forests (p < 0.05). The findings of this study offer empirical evidence to support forest management strategies targeted at enhancing carbon sequestration.

The interacting effect of climate change and herbivory can trigger large-scale transformations of European temperate forests.

In many regions of Europe, large wild herbivores alter forest community composition through their foraging preferences, hinder the forest's natural adaptive responses to climate change, and reduce ecosystem resilience. We investigated a widespread European forest type, a mixed forest dominated by Picea abies, which has recently experienced an unprecedented level of disturbance across the continent. Using the forest landscape model iLand, we investigated the combined effect of climate change and herbivory on forest structure, composition, and carbon and identified conditions leading to ecosystem transitions on a 300-year timescale. Eight climate change scenarios, driven by Representative Concentration Pathways 4.5 and 8.5, combined with three levels of regeneration browsing, were tested. We found that the persistence of the current level of browsing pressure impedes adaptive changes in community composition and sustains the presence of the vulnerable yet less palatable P. abies. These development trajectories were tortuous, characterized by a high disturbance intensity. On the contrary, reduced herbivory initiated a transformation towards the naturally dominant broadleaved species that was associated with an increased forest carbon and a considerably reduced disturbance. The conditions of RCP4.5 combined with high and moderate browsing levels preserved the forest within its reference range of variability, defining the actual boundaries of resilience. The remaining combinations of browsing and climate change led to ecosystem transitions. Under RCP4.5 with browsing effects excluded, the new equilibrium conditions were achieved within 120 years, whereas the stabilization was delayed by 50-100 years under RCP8.5 with higher browsing intensities. We conclude that forests dominated by P. abies are prone to transitions driven by climate change. However, reducing herbivory can set the forest on a stable and predictable trajectory, whereas sustaining the current browsing levels can lead to heightened disturbance activity, extended transition times, and high variability in the target conditions.

An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns

Marine heatwaves (MHWs) refer to a phenomenon where the sea surface temperature is significantly higher than the historical average for that region over a period, which is typically a result of the combined effects of climate change and local meteorological conditions, thereby potentially leading to alterations in marine ecosystems and an increased incidence of extreme weather events. MHWs have significant impacts on the marine environment, ecosystems, and economic livelihoods. In recent years, global warming has intensified MHWs, and research on MHWs has rapidly developed into an important research frontier. With the development of deep learning models, they have demonstrated remarkable performance in predicting sea surface temperature, which is instrumental in identifying and anticipating marine heatwaves (MHWs). However, the complexity of deep learning models makes it difficult for users to understand how the models make predictions, posing a challenge for scientists and decision-makers who rely on interpretable results to manage the risks associated with MHWs. In this study, we propose an interpretable model for discovering MHWs. We first input variables that are relevant to the occurrence of MHWs into an LSTM model and use a posteriori explanation method called Expected Gradients to represent the degree to which different variables affect the prediction results. Additionally, we decompose the LSTM model to examine the information flow within the model. Our method can be used to understand which features the deep learning model focuses on and how these features affect the model’s predictions. From the experimental results, this study provides a new perspective for understanding the causes of MHWs and demonstrates the prospect of future artificial intelligence-assisted scientific discovery.

Can domestic wheat farming meet the climate change-induced challenges of national food security in Uzbekistan?

ABSTRACT This study is the first to develop food supply and demand projections over the 21st century for Uzbekistan by considering the combined effects of climate change and soil salinization. The study results suggest that rising summer temperatures and soil salinity will considerably reduce wheat production. Projections indicate that a large wheat supply–demand gap will emerge in the midterm, particularly under the SSP3-RCP7.0 scenario. For the two more pessimistic scenarios, supply losses of about 24–29% are expected by the end of the century. Supply–demand gaps of up to 2.7 million tons of wheat would pose serious challenges to national food security.

Susceptibility of dwarf chameleons to climate and land use change: a vulnerability framework for conservation planning

Climate and land use changes are eroding biodiversity globally, and reptiles are highlighted as being particularly susceptible. In South Africa, global changes threaten the persistence of an assemblage of dwarf chameleons (Bradypodion) located in a biodiversity hotspot. We used ecological niche modelling to assess the combined effect of climate change and habitat transformation on these species and assessed their susceptibility in a vulnerability framework under optimistic and pessimistic change scenarios. Although our models showed a gain in suitable climatic space for all coastal species in some scenarios, considerable losses were predicted for most species under the most pessimistic change scenarios. Bradypodion ngomeense, for example is predicted to incur a complete loss of climatic suitability by 2050. The vulnerability framework predicts inland species to be more adversely affected by climate change than coastal species. However, no species show resilience to the combined effects of climate change and habitat transformation. Our models predicted a loss of climatically suitable habitat for most species in protected areas. These findings highlight the importance of a protected area network design to remain a step ahead of these anticipated changes.

Evaluating the effects of climate change and chemical, physical, and biological stressors on nearshore coral reefs: A case study in the Great Barrier Reef, Australia.

An understanding of the combined effects of climate change (CC) and other anthropogenic stressors, such as chemical exposures, is essential for improving ecological risk assessments of vulnerable ecosystems. In the Great Barrier Reef, coral reefs are under increasingly severe duress from increasing ocean temperatures, acidification, and cyclone intensities associated with CC. In addition to these stressors, inshore reef systems, such as the Mackay-Whitsunday coastal zone, are being impacted by other anthropogenic stressors, including chemical, nutrient, and sediment exposures related to more intense rainfall events that increase the catchment runoff of contaminated waters. To illustrate an approach for incorporating CC into ecological risk assessment frameworks, we developed an adverse outcome pathway network to conceptually delineate the effects of climate variables and photosystem II herbicide (diuron) exposures on scleractinian corals. This informed the development of a Bayesian network (BN) to quantitatively compare the effects of historical (1975-2005) and future projected climate on inshore hard coral bleaching, mortality, and cover. This BN demonstrated how risk may be predicted for multiple physical and biological stressors, including temperature, ocean acidification, cyclones, sediments, macroalgae competition, and crown of thorns starfish predation, as well as chemical stressors such as nitrogen and herbicides. Climate scenarios included an ensemble of 16 downscaled models encompassing current and future conditions based on multiple emission scenarios for two 30-year periods. It was found that both climate-related and catchment-related stressors pose a risk to these inshore reef systems, with projected increases in coral bleaching and coral mortality under all future climate scenarios. This modeling exercise can support the identification of risk drivers for the prioritization of management interventions to build future resilient reefs. Integr Environ Assess Manag 2024;20:401-418. © 2023 Norwegian Institute for Water Research and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

MEASURING THE INFLUENCE OF A MOTORWAY CONSTRUCTION ON LAND SURFACE TEMPERATURE USING LANDSAT THERMAL DATA: A CASE STUDY IN THE METROPOLITAN CITY OF MILAN

Abstract. Cities have been identified as a landmark for climate change, being among the direct targets of its negative feedbacks. The combined effect of climate change and rapidly growing urbanization is exacerbating the urban heat island phenomenon in cities worldwide. The availability of multiple geo-data sources including satellite remote sensing products is significantly empowering the investigation of its driving factors. This is a crucial step to implement ad hoc mitigation and adaptation strategies. In view of the above, the goal of this study is to measure the effect of a motorway on the Land Surface Temperature (LST) space-time patterns by leveraging Landsat 5 and 8 thermal data of the period from 2006 to 2022. The study area is around the motorway A58 and connected roads in the Metropolitan City of Milan (northern Italy). LST patterns are investigated along the motorway track and in the neighbouring areas before and after the motorway construction, in both the cold and warm seasons. Results show that the motorway significantly affects the LST distribution during summer with a median increase of 2.5 °C along the road track with respect to the surrounding area. The warming effect is also recorded in the road buffers with decreasing LST with increasing distance from the road. On the contrary, no meaningful variation in terms of LST is measured in winter. These experiments provide insightful measures of the effect of a highway on the local climate conditions in an urban area, thus representing crucial pieces of information for driving evidence-based urban planning activities.

Dive industry perspectives on the threats to coral reefs: A comparative study across four Asia-Pacific countries

The combined effects of climate change, marine tourism and other stressors threaten the ecological and economic sustainability of coral reefs. This study investigates dive industry stakeholder awareness of the threats to coral reefs through structured interviews with Dive Masters, company managers and marine management agencies in Vietnam, Australia, Malaysia and Indonesia. Stakeholders from all locations have observed degradation of local reefs. Destructive fishing was identified as the principal threat in all regions except Australia. Most participants identified threats from climate change and marine tourism. There was a lack of awareness about ocean acidification by all participants from Maluku, Indonesia. However, ocean acidification could make coral more fragile and, therefore, vulnerable to diver-induced damage. The majority of Dive Masters across all regions provide pre-dive briefings to reduce diver impacts and participate in environmental activities to protect local reefs. Stakeholders in three regions thought there was capacity to expand the local dive industry. However, in Nha Trang Vietnam, most industry stakeholders thought they were at, or exceeded, carrying capacity, whereas marine management employees thought there was room to expand. This study highlights an opportunity to improve diver education on the vulnerability of coral to damage in acidifying oceans. This study also identifies various non-regulatory and regulatory strategies used to reduce diver impacts, emphasising the value of multi-national knowledge sharing between the dive industry and regulatory agencies for adaptive management.

Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution

A fine global future land use/land cover (LULC) is critical for demonstrating the geographic heterogeneity of earth system dynamics and human-earth interaction. In this study, we produced a 1 km global future LULC dataset that takes into account future climate and socio-economic changes as well as the impact of simulated results of the former year on temporally adjacent periods. By incorporating the variations in climatic and socio-economic factors, we differentiated LULC suitability probabilities for historical and future periods across representative SSP-RCP scenarios. Then, by using an improved cellular automata model-PLUS to simulate the patch-level changes of various land classes, we iteratively downscaled water-basin-level LULC demands in various future scenarios to a spatial resolution of 1 km. Our dataset achieves a high degree of simulation accuracy (Kappa = 0.94, OA = 0.97, FoM = 0.10) and precisely captures the spatial-temporal heterogeneity of global LULC changes under the combined effects of climate change and socio-economic development. This robust and fine-scale LULC dataset provides valuable spatially-explicit information essential for earth system modeling and intricate dynamics between anthropogenic activities and the environment.

Editorial: Exploring the combined effect of climate change and pollution on freshwater ecosystems

Editorial: Exploring the combined effect of climate change and pollution on freshwater ecosystems

Combined effects of climate change and agricultural intensification on soil erosion in uphill shifting cultivation in Northeast India

AbstractShifting cultivation will face increasing pressure from erosion‐related land degradation caused by rising cultivation intensities and climate change. However, empirical knowledge about future trends of soil erosion and thus land degradation in shifting cultivation systems is limited. We use the Environmental Policy Integrated Climate (EPIC) model to first explore the combined effects of climate change and agricultural intensification on soil erosion of uphill shifting cultivation systems, using six surveyed soil profiles. We assess interactions between climate change, the length of the fallow period, and slope inclinations for a near (2021–2050) and far (2071–2100) future period, considering three climate scenarios, five climate models, fallow periods between one and 20 years, and slopes between five and 70% steepness. Our results show a significant nonlinear relationship between global warming and erosion. Until the end of the century, erosion is estimated to increase by a factor of 1.2, 2.2, and 3.1 under the SSP126, SSP370, and SSP585 scenarios, respectively, compared with the historical baseline (1985–2014). Combined effects from climate change, fallow length, and slope inclination indicate that steep slopes require longer fallow periods, with an increase of slope from 5% to 10% multiplying the required fallow length by a mean factor of 2.5, and that fallow periods will need to be extended under higher global warming if erosion rates are to remain at current levels. These findings are novel as they link climate change effects on shifting cultivation systems to different slopes and fallow regimes, making an important contribution to understanding future erosion dynamics of traditional smallholder production systems in mountainous terrain, with relevant implications for policies on agricultural intensification.