Future Climate Change Research Articles

Extreme precipitation and temperature indices under future climate change in central Asia based on CORDEX-CORE

Extreme precipitation and temperature indices under future climate change in central Asia based on CORDEX-CORE

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.

Predicting river phytoplankton blooms and community succession using ecological niche modeling

AbstractExcessive phytoplankton concentrations in rivers can result in the loss of plant and invertebrate communities, and threaten drinking water supplies. Whilst the physicochemical controls on algal blooms have been identified previously, how these factors combine to control the initiation, size, and cessation of blooms in rivers is not well understood. We applied flow cytometry to quantify diatom, chlorophyte, and cyanobacterial group abundances in the River Thames (UK) at weekly intervals from 2011 to 2022, alongside physicochemical data. A niche modeling approach was used to identify thresholds in water temperature, flow, solar radiation, and soluble reactive phosphorus (SRP) concentrations required to produce periods of phytoplankton growth, with blooms only occurring when all thresholds were met. The thresholds derived from the 2011 to 2018 dataset were applied to a test data set (2019–2022), which predicted the timing and duration of blooms at accuracies of > 80%. Diatoms and nano‐chlorophyte blooms were initiated by flow and water temperature, and usually terminated due to temperature and flow going out of the threshold range, or SRP and Si becoming limiting. Cyanobacterial bloom dynamics were primarily controlled by water temperature and solar radiation. This simple methodology provides a key understanding of phytoplankton community succession and inter‐annual variation and can be applied to any river with similar water quality and phytoplankton data. It provides early warnings of algal and cyanobacterial bloom timings, which support future catchment management decisions to safeguard water resources, and provides a basis for modeling changing phytoplankton bloom risk due to future climate change.

Physiological effects of interacting native and invasive bivalves under thermal stress

AbstractAcross many ecosystems in North America and Europe, native freshwater bivalves (Order Unionida) are threatened by fouling and competition for food by the invasive zebra mussel Dreissena polymorpha. In light of climate change, knowledge on the influence of water temperature on these competitive effects is important, yet poorly understood. This study examines the physiological impact of the interaction between D. polymorpha and the native European unionid Anodonta cygnea over a 28 day—period in response to water temperatures of 12, 19, and 25 °C by comparing their glycogen, glucose, lipid and protein concentrations. The laboratory experiment comprised three treatments: (1) fouling of A. cygnea by D. polymorpha, (2) both species present but not fouling; and (3) a control in which A. cygnea and D. polymorpha were placed separately. Increased water temperatures caused physiological stress in D. polymorpha as evident from reduced glycogen, glucose, lipid and protein concentrations. Dreissena polymorpha benefited from fouling of unionids, as individuals that fouled A. cygnea tended to have increased glycogen, glucose, lipid and protein concentrations. Competitive effects of D. polymorpha over the unionid bivalve species, however, were not intensified by elevated temperatures. Glochidia release, lower infestation intensity, and physiological stress of Dreissena at higher temperatures were likely confounding factors. The results of this study suggest that understanding the physiological consequences of species interactions at changing temperatures can be an important tool to assess future climate change impacts on freshwater bivalves and aquatic community structures.

Predicting the current and future suitable distribution range of Trilocha varians (Walker, 1855) (Lepidoptera: Bombycidae) in China.

Trilocha varians is one of the major pests of Ficus spp. Based on 19 bioclimatic variables provided by the Worldclim, our study analysed the suitable distribution areas of T. varians under current and future climate changes (SSP1-2.6, SSP2-4.5, SSP5-8.5) for two periods (the 2050s and 2090s) using the maximum entropy algorithm (MaxEnt) model. Key environmental variables affecting the geographic distribution of T. varians were also identified, and the changes in the area of suitable range under current and future climate changes were compared. The results showed that the key environmental variables affecting the distribution of T. varians were temperature and precipitation, comprising annual mean temperature (bio1), temperature seasonality (standard deviation × 100) (bio4), precipitation of driest month (bio14), and precipitation of driest quarter (bio17). Under the current climatic conditions, the suitable distribution area of T. varians is within the range of 92°13'E-122°08'E, 18°17'N-31°55'N. The current high, medium, and low suitable areas for T. varians predicted by the MaxEnt model are 14.00 × 104, 21.50 × 104, and 71.95 × 104 km2, of which the high suitable areas are mainly distributed in southern Guangdong, southwestern Guangxi, western Taiwan, Hong Kong, and Hainan. Under different future climatic conditions, some of the high, medium, and low suitability zones for T. varians increased and some decreased, but the mass centre did not migrate significantly. The Pearl River Basin is predicted to remain the main distribution area of T. varians.

Fluviomorphic trajectories for dryland ephemeral stream channels following extreme flash floods

AbstractEphemeral alluvial streams pose globally significant flood hazards to human habitation in drylands, but sparse data for these regions limit understanding of the character and impacts of extreme flooding. In this study, we document decadal changes in dryland ephemeral channel patterns at two sites in the lower Colorado River Basin (southwestern United States) that were ravaged by extraordinary flash floods in the 1970s: Bronco Creek, Arizona (1971), and Eldorado Canyon, Nevada (1974). We refer to these two floods as ‘fluviomorphic erasure events’, because they produced blank slates for the channels that were gradually moulded by more frequent but much smaller flood events. We studied georectified aerial photos that span ~60 years at each site to show that both study sites recovered to their pre‐flood condition after ~25 years. We employ channel network metrics: stream‐link area (SLA), geometric braiding index and junction‐node density. Each metric decreased during the short‐duration extreme flood erasure events. Subsequently, a fluviomorphic trajectory at a decadal tempo returned the channels to pre‐flood values. The SLA decreased at rates of 3.6%–4.1% per year in the decade following the floods. The extreme flood events decreased the pre‐flood geometric braiding index at the two sites by 56%–68%, and it took 15–24 years for this index to recover to pre‐flood values. In contrast, it took 30–35 years for the channels to recover to a uniform pre‐flood channel form, as indicated by the spatial distribution of bars and junction nodes. Our results document baseline examples of ephemeral stream channel evolution trajectories, as future climatic change will likely accelerate increases in the magnitudes and frequencies of extreme floods and geomorphic erasure events.

Projected habitat preferences of commercial fish under different scenarios of climate change

The challenges of commercial species with the threats of climate change make it necessary to predict the changes in the distributional shifts and habitat preferences of the species under possible future scenarios. We aim to demonstrate how future climatic changes will affect the habitat suitability of three species of commercial fish using the predictive technique MaxEnt. The dataset used to extract geographical records included OBIS (54%), GBIF (1%), and literature (45%). The output of the model indicated accurate projections of MaxEnt (AUC above 0.9). Temperature was the main descriptor responsible for the main effects on the distribution of commercial fish. With increasing RCP from 2.5 to 8.5, the species would prefer saltier, higher temperatures and deeper waters in the future. We observed different percentages of suitable habitats between species during RCPs showing distinct sensitivity of each fish in facing climate changes. Negative effects from climate change on the distribution patterns of commercial fish were predicted to lead to varying degrees of reduction and changes of suitable habitats and movement of species towards higher latitudes. The finding emphasizes to implement adaptive management measures to preserve the stocks of these commercial fish considering that the intensification of the effects of climate change on subtropical areas and overexploited species is predicted.

Vulnerability assessment of Taxus wallichiana in the Indian Himalayan Region to future climate change using species niche models and global climate models under future climate scenarios

Vulnerability assessment of Taxus wallichiana in the Indian Himalayan Region to future climate change using species niche models and global climate models under future climate scenarios

Relationships between snowpack, low flows and stream temperature in mountain watersheds of the US west coast

AbstractWater temperatures in mountain streams are likely to rise under future climate change, with negative impacts on ecosystems and water quality. However, it is difficult to predict which streams are most vulnerable due to sparse historical records of mountain stream temperatures as well as complex interactions between snowpack, groundwater, streamflow and water temperature. Minimum flow volumes are a potentially useful proxy for stream temperature, since daily streamflow records are much more common. We confirmed that there is a strong inverse relationship between annual low flows and peak water temperature using observed data from unimpaired streams throughout the montane regions of the United States' west coast. We then used linear models to explore the relationships between snowpack, potential evapotranspiration and other climate‐related variables with annual low flow volumes and peak water temperatures. We also incorporated previous years' flow volumes into these models to account for groundwater carryover from year to year. We found that annual peak snowpack water storage is a strong predictor of summer low flows in the more arid watersheds studied. This relationship is mediated by atmospheric water demand and carryover subsurface water storage from previous years, such that multi‐year droughts with high evapotranspiration lead to especially low flow volumes. We conclude that watershed management to help retain snow and increase baseflows may help counteract some of the streamflow temperature rises expected from a warming climate, especially in arid watersheds.

Declined benefit of earlier spring greening on summer growth in northern ecosystems under future scenarios

Declined benefit of earlier spring greening on summer growth in northern ecosystems under future scenarios

Future Climate Change Impacts on Rice in Uttar Pradesh, India's Most Populous Agrarian State

AbstractUttar Pradesh, with a population of 237 million, is the largest agrarian state in India, located in the Indo‐Gangetic plains. Rice cultivation is widespread across all districts of Uttar Pradesh, which have varying climate regimes, irrigation infrastructures, crop management practices, and farm sizes. The state is characterized by different agroecological zones (AEZs) with semi‐arid to sub‐humid climates with significant variability in monsoon rainfall. In this study, the impact of climate change on Kharif‐season rice is estimated using crop‐climate scenarios in Uttar Pradesh. A process‐based Crop Simulation Model, Crop Estimation through Resource and Environment Synthesis‐Rice, was simulated with bias‐corrected and downscaled climate data for historical (1995–2014) and three future periods (the 2030s, 2050s, and 2090s) for two mitigation pathways (SSP2‐4.5 and SSP5‐8.5) from the Coupled Model Intercomparison Project 6. Phenology, irrigation amount, crop evapotranspiration, yield, and water use efficiency were evaluated and assessed for all AEZs. Based on the ensemble of 16 climate models, rainfed rice yield increased in the AEZs of western Uttar Pradesh due to increased rainfall, while in eastern Uttar Pradesh yield decreased, under both shared socioeconomic pathways (SSPs). Irrigated rice yield decreased in all AEZs under both SSPs due to an increase in temperature and a decrease in the length of the growing period, with reductions of up to 20% by the 2090s. Irrigation requirements decreased from the 2030s to the 2090s due to increased rainfall and decreased crop evapotranspiration. Despite the projected increase in rainfed yield, the overall rice yield is expected to decrease in the future under both SSPs.

Seedling dynamics differ between canopy species and understory species in a tropical seasonal rainforest, SW China

We used 11 years of census data from 450 seedling quadrats established in a 20-ha forest dynamics plot to study seedling dynamics in tree species of a tropical seasonal rainforest in Xishuangbanna, southwestern China. We found that overall seedling recruitment rate and relative growth rate were higher in the rainy season than in the dry season. Both the recruitment rate of seedlings from canopy tree species (two species) and the relative growth rate of seedlings from understory species (nine species) were higher in the rainy season than in the dry season. However, in the rainy season, the recruitment rate of seedlings was higher for canopy tree species than for understory tree species. In addition, relative growth rate of seedlings was higher in the canopy species than in understory seedlings in the dry season. We also observed that, in both rainy and dry seasons, mortality rate of seedlings was higher for canopy species than for understory species. Overall, canopy tree species appear to have evolved a flexible strategy to adapt to the seasonal changes of a monsoon climate. In contrast, understory tree species seem to have adopted a conservative strategy. Specifically, these species mainly release seedlings in the rainy season and maintain relatively stable populations with a lower mortality rate and recruitment rate in both dry and rainy seasons. Our study suggests that canopy and understory seedling populations growing in forest understory may respond to future climate change scenarios with distinct regeneration strategies.

NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps

Abstract. Models of rocky-coast erosion help us understand the physical phenomena that control coastal morphology and evolution, infer the processes shaping coasts in remote environments, and evaluate risk from natural hazards and future climate change. Existing models, however, are highly complex, are computationally expensive, and depend on many input parameters; this limits our ability to explore planform erosion of rocky coasts over long timescales (thousands to millions of years) and over a range of conditions. In this paper, we present a simplified cellular model of coastline evolution in closed basins through uniform erosion and wave-driven erosion. Uniform erosion is modeled as a constant rate of retreat. Wave erosion is modeled as a function of fetch, the distance over which the wind blows to generate waves, and the angle between the incident wave and the shoreline. This reduced-complexity model can be used to evaluate how a detachment-limited coastal landscape reflects climate, sea-level history, material properties, and the relative influence of different erosional processes.

Changes in the Typhoon Intensity under a Warming Climate: A Numerical Study of Typhoon Mangkhut

Abstract Accurately assessing cyclone intensity changes due to global warming is crucial for predicting and mitigating sequential hazards. This study develops a high-resolution, fully coupled air-sea model to investigate the impact of global warming on Super Typhoon Mangkhut (2018). A numerical sensitivity analysis is conducted using the Pseudo-Global Warming (PGW) technique based on multiple global climate models (GCMs) from the Coupled Model Intercomparison Project Phases 6 (CMIP6). Under ocean warming scenarios, the increasing average sea surface temperature (SST) by 2.26 °C, 2.44 °C, 3.45 °C, and 4.53 °C result in reductions in minimum sea-level pressure by 9.2 hPa, 10.6 hPa, 15.7 hPa, and 19.4 hPa, respectively, compared to the original state of Typhoon Mangkhut. Rising SST increases turbulent heat flux, to be specific, an average SST increase of 2.26-4.53 °C changes the turbulent heat flux into 177% to 272% of the original value. Besides, stronger winds enhance SST cooling, including upwelling and entrainment, leading to an increase in the mixed layer depth (MLD). Tropical cyclone heat potential (TCHP) tends to be enhanced under the combined influences as the SST rises. An average increase in SST of 2.26 °C, 2.44 °C, 3.45 °C, and 4.53 °C leads to increase in TCHP of 9.94%, 9.85%, 14.67%, and 15.30%, respectively. However, future changes in atmospheric temperature and humidity will moderate typhoon intensification induced by ocean warming. Considering atmospheric conditions, the maximum wind speed decreases by approximately 10% compared to only considering ocean warming. Nevertheless, typhoon intensity is projected to strengthen under future climate change.

Improved biomass burning emissions from 1750 to 2010 using ice core records and inverse modeling

Estimating fire emissions prior to the satellite era is challenging because observations are limited, leading to large uncertainties in the calculated aerosol climate forcing following the preindustrial era. This challenge further limits the ability of climate models to accurately project future climate change. Here, we reconstruct a gridded dataset of global biomass burning emissions from 1750 to 2010 using inverse analysis that leveraged a global array of 31 ice core records of black carbon deposition fluxes, two different historical emission inventories as a priori estimates, and emission-deposition sensitivities simulated by the atmospheric chemical transport model GEOS-Chem. The reconstructed emissions exhibit greater temporal variabilities which are more consistent with paleoclimate proxies. Our ice core constrained emissions reduced the uncertainties in simulated cloud condensation nuclei and aerosol radiative forcing associated with the discrepancy in preindustrial biomass burning emissions. The derived emissions can also be used in studies of ocean and terrestrial biogeochemistry.

Divergent responses of subtropical evergreen and deciduous forest carbon cycles to the summer 2022 drought

Ongoing shifts in climate, especially extreme drought, is posing considerable threats to the forest carbon uptake worldwide. In China, summer 2022 was the warmest and driest since the beginning of meteorological measurements. This study synthesized the tower-based carbon fluxes and climate data from two subtropical evergreen and deciduous forest ecosystems to investigate the effects of such summer drought. Interestingly, the net ecosystem production (NEP = −NEE) only exhibited a slight decrease at the deciduous forest while it even enhanced at the evergreen forest during the summer 2022 drought. Further analysis revealed that although reductions in gross primary productivity (GPP) and ecosystem respiration (R e) were found at both sites, larger decrease in R e than GPP at the evergreen forest led to stronger NEP compared to the previous year. However, the NEP of two forest ecosystems sharply reduced in the following 2023, which can be ascribed to the legacy effects of the summer 2022 drought. The results of multiple linear regression revealed that soil water content (SWC) was recognized as the primary driver of GPP and R e, and downwelling shortwave radiation (R g) regulated the variability of NEP during the summer 2022. Across these forest carbon fluxes including GPP, R e and NEP, the deciduous forest exhibited larger resistance, whereas the evergreen forest showed stronger resilience. All analyses emphasized the diverse adaptive strategies among vegetation types, which acted an important role in assessing ecosystem carbon sequestration in face of future climate change.

Drought response modelling of leaf photosynthetic parameters in two Gossypium species

AbstractCotton is well adapted to dry areas, but progressive water deficits can lead to declines in net photosynthesis (A), ultimately reducing yield. However, the exact mechanism responsible for this decline in net photosynthesis (stomatal or non‐stomatal) is not fully understood under field conditions, partially due to limitations in the ability to collect critical data. To our knowledge, no other study has compared the drought responses of Pima and upland cotton using both CO2 response and chlorophyll fluorescence under field conditions. To this end, a field study was conducted to quantify the impact of progressive mild drought, as measured by midday stomatal conductance to water vapour (gs), on cotton leaf metabolism in Pima and upland cotton. Survey gas exchange and rapid photosynthetic CO2 response (RACiR) were conducted during flowering on the same leaf. The study observed decline in A as gs declined for both species. Correlation analysis indicated typical relationships with A and parameters associated with stomatal limitations such as decreased CO2 inside the leaf and at the site of carboxylation; however, it was found that while Pima exhibited a strong relationship between maximum electron transport rate (Jmax) and electron transport rate (ETR), upland cotton did not. Furthermore, when ETR is broken down into proportions contributing to net photosynthesis and photorespiration (ETRA, ETRP, respectively), we found that a greater proportion of ETR is being shuttled to the photorespiratory pathway in upland, relative to Pima as gs decreases. Our results fill critical knowledge gaps that can be useful for modellers and breeders when preparing for future climate change scenarios.

Predicting the suitable cultivation areas of breadfruit crops Artocarpus altilis (Moraceae) under future climate scenarios in Central Java, Indonesia

Artocarpus altilis, commonly known as breadfruit, is a potential crop adapted to a wide variety of climates and widely spread, including in Indonesia. However, information on how this species can adapt to climate change, in particular in Central Java, is still limited. In Indonesia, Central Java is the center for cultivation areas for many crop species to support the 145 million people living on Java Island. One of the potential crops being developed in Central Java is breadfruit. To assess the suitable cultivation areas for breadfruit, species distribution modeling (SDM) was used to predict the current and future (2050–2070) distribution of breadfruit. Two climate change scenarios, including optimistic RCP2.6 and pessimistic RCP8.5 models, were considered to represent future climate change impacts. Based on the results for both optimistic and pessimistic scenarios, the breadfruit’s suitable cultivation areas will expand eastward. Implementing a mitigation climate change scenario and limiting the temperature increase to only 1°C under RCP2.6 will provide 270.967 km2 more of suitable cultivation areas for breadfruit in 2050 and 133.296 km2 in 2070. To conclude, this study provides important information on the status and potential cultivation areas for breadfruit, mainly in the Southeast Asia region. The identification of suitable areas will guide land conservation for breadfruit to support food security in this region.

California’s 2023 snow deluge: Contextualizing an extreme snow year against future climate change

The increasing prevalence of low snow conditions in a warming climate has attracted substantial attention in recent years, but a focus exclusively on low snow leaves high snow years relatively underexplored. However, these large snow years are hydrologically and economically important in regions where snow is critical for water resources. Here, we introduce the term "snow deluge" and use anomalously high snowpack in California's Sierra Nevada during the 2023 water year as a case study. Snow monitoring sites across the state had a median 41 y return interval for April 1 snow water equivalent (SWE). Similarly, a process-based snow model showed a 54 y return interval for statewide April 1 SWE (90% CI: 38 to 109 y). While snow droughts can result from either warm or dry conditions, snow deluges require both cool and wet conditions. Relative to the last century, cool-season temperature and precipitation during California's 2023 snow deluge were both moderately anomalous, while temperature was highly anomalous relative to recent climatology. Downscaled climate models in the Shared Socioeconomic Pathway-370 scenario indicate that California snow deluges-which we define as the 20 y April 1 SWE event-are projected to decline with climate change (58% decline by late century), although less so than median snow years (73% decline by late century). This pattern occurs across the western United States. Changes to snow deluge, and discrepancies between snow deluge and median snow year changes, could impact water resources and ecosystems. Understanding these changes is therefore critical to appropriate climate adaptation.

Headlining mental health in a changing climate: a systematic analysis of climate change and mental health news coverage from Canada and the United States

Abstract Climate change exerts a profound and far-reaching toll on mental health, resulting in mental illness, diminished wellbeing, and strained social relations, with the severity of these impacts anticipated to intensify under future climate change. News coverage of climate-mental health issues can shape public perceptions and responses, and therefore understanding this coverage is vital for creating and implementing strategies to lessen climate-mental health risks. We systematically examined the extent, nature, and range of news articles from Canada and the United States that covered climate-mental health impacts. Five electronic databases were searched. Articles were included if they were print or online news stories that covered climate-mental health impacts and were published in English or French by sources in Canada or the United States between 2016 and 2020. A total of 1,085 articles (642 unique stories) met the inclusion criteria and underwent data extraction and analysis using descriptive statistics and thematic analysis. Coverage of climate-mental health outcomes increased in both Canada and the United States from 2016 to 2019. There was, however, a substantial decrease in coverage by Canadian news outlets from 2019 to 2020. Further, although 468 different news outlets published at least one story on the topic, repeat coverage by any one news outlet was infrequent. Most opinion pieces presented climate-mental health impacts as legitimate or expressed support for people experiencing these issues. Roughly half of the included stories identified populations that are vulnerable to climate-mental health impacts, particularly children and young people. Additionally, approximately half of the included stories identified potential interventions or coping strategies to protect mental health, most often involving changes to individual lifestyles or behaviour. More sustained coverage that focuses on protective factors, interventions, and coping strategies is needed, as this could help foster public understanding and drive action to reduce climate-mental health risks.