Exchange Global Daily Downscaled Projections Research Articles

The comprehensive effects of future multi‐scenario land use change and climate change on water conservation in Northwest China

AbstractWater conservation is an important ecosystem service, which is crucial to the fragile ecological environment and serious soil erosion in Northwest China. However, water conservation has been affected by climate change and human disturbance, and its future spatial and temporal changes are still unclear. This study employed the Integrated Valuation of Ecosystem Services and Trade‐offs (InVEST) model to assess the water conservation capacity of Northwest China between 1990 and 2020. Subsequently, the Patch‐generating Land Use Simulation (PLUS) model and the NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP‐CMIP6) were integrated to examine changes in water conservation from 2030 to 2050. The water conservation quantity in Northwest China had a fluctuating growth tendency between 1990 and 2020, with an average of 816.61 × 108 m3. The water conservation capacity's regional distribution pattern shows a steady increase from northwest to southeast. Northwest China's forest land had the greatest water conservation capacity between 1990 and 2020, at 66 mm, while the water land had the lowest, at 1.46 mm. The water conservation capacity in the 2030–2050 future scenario is less than that in 2020 when the regional distribution pattern is comparable to 2020. Natural development scenario (ND) in 2030 has the lowest water conservation capacity (13.41 mm) under ssp1‐2.6 precipitation scenario, whereas in 2050, the highest water conservation capacity (17.47 mm) is found in same scenario. The ecological protection scenario (EP) has the greatest water retention capacity (18.15 mm in 2040) and the lowest (14.38 mm in 2050) in the ssp5‐8.5 scenario. Our research can help restore and rebuild Northwest China's natural ecosystem and offer a theoretical foundation for the protection of ecological environment and the rational utilization of water resources in the region.

Improving simulations of extreme precipitation events in China by the CMIP6 global climate models through statistical downscaling

The recently released NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is a high-resolution daily downscaled dataset derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations. We comprehensively evaluated the performance of the NEX-GDDP dataset in simulating the characteristics of the climatological precipitation and extreme precipitation events across China from 1960 to 2014. We present here the projected changes in precipitation at the end of the 21st century (2070–2099) with respect to a reference period (1985–2014). We compared the NEX-GDDP dataset with both a state-of-the-art gauge data analysis system and the CMIP6 global climate models. The NEX-GDDP dataset significantly outperformed the CMIP6 simulations in replicating the climatological patterns of precipitation. It showed a closer alignment with the observed data, as evidenced by notably increased correlation coefficients and reduced model-relative errors. The NEX-GDDP dataset exceled in accurately reproducing the spatial distribution of indices of extreme precipitation, surpassing the performance of the CMIP6 simulations. The projections show an increased frequency of heavy precipitation under higher emission scenarios in both datasets, with the CMIP6 simulations consistently estimating a higher probability of heavy rain than the NEX-GDDP dataset.

Increased population exposures to extreme precipitation in Central Asia under 1.5℃ and 2℃ global warming scenarios

The increase in extreme precipitation (EP) may pose a serious threat to the health and safety of population in arid and semi-arid regions. The current research on the impact of EP on population in Central Asia (CA) is insufficient and there is an urgent need for a comprehensive assessment. Hence, we opted for precipitation and temperature data under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) from ten Global Climate Models (GCMs), which were obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). Simultaneously, integrating population data in 2020 and 2050 (SSP2 and SSP5), we investigated the future changes in EP and population exposure in CA under 1.5°C and 2°C global warming scenarios (GWSs). Our analysis indicates that EP in CA is projected to increase with global warming. Under the SSP5-8.5, the maximum daily precipitation (Rx1day) exhibits an average response rate to global warming of 3.58 %/K (1.99 %/K – 4.06 %/K). With rising temperatures, an increasing number of areas and populations in CA will be impacted by EP, especially in the Fergana valley. Approximately 25% of the population (land area) in CA is exposed to Rx1day with increases of more than 8.31% (9.32%) under 1.5°C GWS and 14.18% (13.25%) under 2°C GWS. Controlling temperature rise can be effective in reducing population exposures to EP. For instance, limiting the temperature increase to 1.5°C instead of 2°C results in a 2.79% (1.75%–4.59%) reduction in population exposure to Rx1day. Finally, we found that climate change serves as the predominant factor influencing the population exposure to EP, while the role of population redistribution, although relatively minor, should not be disregarded. Particularly for prolonged drought, the role of population redistribution manifests negatively.

Projected population exposure to heatwaves in Xinjiang Uygur autonomous region, China.

The intensification of heatwaves dues to climate change is a significant concern, with substantial impacts on ecosystems and human health, particularly in developing countries. This study utilizes NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) and projected population data accounting for China's population policies to project changes in various grades of heatwaves (light, moderate, and severe) and the population exposure to heatwaves (PEH) in Xinjiang under three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The results show that the number of days and intensity of heatwaves in Xinjiang are projected to increase. Heatwaves occurring in Xinjiang will predominantly be severe heatwaves (SHW) in the long-term under the SSP5-8.5 scenario, and the number of SHW days projected to increase by 62 ± 18.4days compared to the reference period. Changes in heatwaves are anticipated to influence PEH, estimating population exposure to light, moderate, and severe heatwaves (LPEH, MPEH, and SPEH) at 534.6 ± 64 million, 496.2 ± 43.5 million, and 1602.4 ± 562.5 million person-days, respectively, in the long-term under the SSP5-8.5 scenario. The spatial distribution of PEH is projected to be consistent with that of the reference period, with high values persisting in Urumqi, Kashgar and Hotan. Changes in PEH are primarily driven by climate effects, followed by interactive effects, while population effects contribute the least. Therefore, mitigating climate change is crucial to reduce the PEH in Xinjiang.

Suitable areas for temperate fruit trees in a Brazilian hotspot area: Changes driven by new IPCC scenarios

Chilling accumulation and sufficient water availability are mandatory requirements for most temperate fruit trees (TFT) productivity. Given the strong dependence of these crops on air temperature and water availability, it is imperative to analyze the impacts of climate change on TFT, especially in areas considered persistent climate change hotspots. This study analyzed the impacts of climate change on suitable areas for planting TFT in a Brazilian state considered a hotspot for future climate changes - Minas Gerais, using an Agroclimatic Zoning (AZ) method, and outlined adaptation measures for the sustainable cultivation of the main TFT under future climate scenarios. Daily near-surface air temperature (minimum and maximum) and precipitation data were used from 10 Earth System Models (ESMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), which are derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6), in two Shared Socioeconomic Pathways scenarios (SSP2–4.5 and SSP5–8.5). The AZ method was applied for the current climate simulations (1995–2014), as well as for projections by the near (2041–2060) and far future (2081 – 2100) climate conditions considering two base temperature values (Tb = 7ºC and 13ºC). Given intense and persistent increases in air temperature, water deficit, and reduced chilling accumulation, TFT plantations will be restricted and economically unfeasible for about 69% (SSP2.4–5) to 86% (SSP5.8–5) of Minas Gerais in the far future for both Tb values. Only ∼6% (near future) and ∼2% (far future) of the study area located in a small portion of the Southern region of Minas Gerais will be suitable for TFT plantations. However, the future of TFT plantations in places classified as regular will depend on adopting adaptation measures to reduce vulnerability to these crops, including low-chill genotypes selection, improvements in breeding programs, using strategies to break dormancy artificially, and intensifying irrigation with less water-demanding techniques.

Future changes in the precipitation regime over the Arabian Peninsula with special emphasis on UAE: insights from NEX-GDDP CMIP6 model simulations

Global warming can profoundly influence the mean climate over the Arabian Peninsula, which may significantly influence both natural and human systems. The present study aims to investigate the changes in the precipitation regime in response to climate change over the Arabian Peninsula, with special emphasis on the United Arab Emirates (UAE). This work is performed using a sub-set of high-resolution NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) data derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models under three different Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The changes are analyzed in three phases such as 2021–2050 (near future), 2051–2080 (mid future) and 2080–2100 (far future), with the period of 1985–2014 as the baseline. This study represents the first attempt to utilize data from NEX-GDDP models to project the regional patterns of precipitation regime across the Arabian Peninsula. Results suggest that the annual precipitation is expected to increase over most of the UAE by up to 30%, particularly intense from the mid-future onwards in all scenarios. Specifically, the spatiotemporal distribution of precipitation extremes such as intensity, 1-day highest precipitation, and precipitation exceeding 10 mm days are increasing; in contrast, the consecutive dry days may decrease towards the end of the century. The results show that the changes in extreme precipitation under a warming scenario relative to the historical period indicate progressive wetting across UAE, accompanied by increased heavy precipitation events and reduced dry spell events, particularly under the high emission scenarios. A high-resolution dataset is essential for a better understanding of changes in precipitation patterns, especially in regions where more detailed information is needed on a local scale to achieve water, food security, and environmental sustainability to formulate effective adaptation strategies for mitigating the potential risks and consequences associated with variations in wet and dry conditions.

Assessing hydrological response to future climate change in the Bouregreg watershed, Morocco

Hydrological systems in watersheds encounter growing challenges attributed to climate change, marked by shifts in precipitation patterns, heightened drought severity, and altered runoff dynamics. This study explores the implications of these changes for streamflow in the vulnerable Bouregreg catchment.This study used a novel approach in the Bouregreg watershed to address this issue by applying the underutilized GR2M model, deviating from the prevalence of ArcSWAT and other models in previous studies. It incorporates bias-corrected outputs from the CNRM-CM5 climate model, sourced from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). The objective was to project monthly streamflow for the 2040s (2030-2050) and compare it with the reference period (1982-2001), considering the Representative Concentration Pathways (RCPs): medium RCP4.5 and high RCP8.5. The methodology started with predicting future precipitation and temperatures using the regional climate model. Then, the future average rainfall for each sub-basin was determined using the Thiessen method, and the future potential evapotranspiration (PET) was estimated through the Thornthwaite formula, considering the model-predicted temperatures. Following this, the hydrological model was calibrated and validated, leading to streamflow projection for the 2040s period under the two emission scenarios.The choice to utilize climate models and hydrological modeling techniques was driven by the need to provide valuable insights for water resource management. By projecting future streamflow under different emission scenarios, the study aimed to inform decision-makers about the potential impacts of climate change on water resources.The study's findings revealed that significant climatic changes would occur in the Bouregreg catchment under the RCPs. Projections indicated a noticeable increase in mean temperatures, with expected rises of about 1.32°C for RCP4.5 and 1.69°C for RCP8.5. Additionally, the PET is foreseen to rise by 5.38 mm under RCP4.5 and 6.27 mm under RCP8.5, while precipitation levels are anticipated to experience significant reductions, decreasing by 33.74% for RCP4.5 and 40.20% for RCP8.5. These pronounced climate alterations are projected to lead to an annual decrease in streamflow. For RCP4.5, this reduction is estimated at 44.63%, and for RCP8.5, it is even more significant at 64.30%. The alterations projected for the 2040s signify a substantial departure from the conditions observed during the reference period of 1982-2001.The importance of these findings lies in their utility for decision-makers in developing effective strategies and facilitating the implementation of adaptation measures for managing water resources and ensuring their sustainable use amid evolving climate conditions.

Future Global Population Exposure to Record‐Breaking Climate Extremes

AbstractThe increase in record‐breaking extreme events caused by climate change poses a threat to human health and well‐being. Understanding the future impacts of such events on global populations can provide decision‐making support for policies aiming to mitigate climate change. Here, we investigated the population exposure to eight climate extreme indices and drivers of exposure trajectories based on National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections Coupled Model Intercomparison Project 6 and population projection data under four shared socioeconomic pathway scenarios at a spatial resolution of 0.25° × 0.25°. The results show that by the mid‐twenty‐first century, most regions worldwide, especially Africa and South America, will continue to experience record‐breaking temperatures and compound drought and heatwaves (CDHWs). Regarding population exposure, under SSP3‐7.0 in the late twenty‐first century, the mean value of the multimodel median expected annual exposure (EAE) of all extreme temperature indices and CDHW reaches 8.12 billion persons per year. Population exposure hotspots will be concentrated in Central Africa, South Asia, Southeast Asia, and East Asia, mostly in developing countries, where 55.01%–87.42% of the EAE is found. The drivers of exposure trajectories are spatially heterogeneous. The increase in record‐breaking probability contributes more than population growth to EAE growth in most regions of the world except Central Asia, the Middle East, and most of Africa. These findings reveal the future trajectories of record‐breaking probabilities and population exposures for climate extremes, which can inform understanding of the intersections between climate change and population change and future risk management.

Meteorological and Hydrological Drought Risks under Future Climate and Land-Use-Change Scenarios in the Yellow River Basin

The primary innovation of this study lies in the development of an integrated modeling framework that combines downscaled climate projections, land-use-change simulations, and copula-based risk analysis. This framework allows for the assessment of localized sub-seasonal and seasonal drought hazards under future scenarios. The BCC-CSM1-1 climate model projections from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset are utilized to represent the future climate for 2025–2060 under RCP 4.5 and 8.5 scenarios. The CA-Markov model is employed to predict future land-use-change distributions. The climate–land use–drought modeling nexus enables the generation of refined spatio-temporal projections of meteorological and hydrological drought risks in the Yellow River Basin (YRB) in the future period of 2025–2060. The results highlight the increased vulnerability of the upper YRB to sub-seasonal meteorological droughts, as well as the heightened sub-seasonal hydrological drought risks in the Loess Plateau. Furthermore, downstream areas experience escalated seasonal hydrological drought exposure due to urbanization. By providing actionable insights into localized future drought patterns, this integrated assessment approach advances preparedness and climate adaptation strategies. The findings of the study enhance our understanding of potential changes in this integral system under the combined pressures of global climate change and land use shifts.

Climate risks and vulnerabilities of the Arabica coffee in Brazil under current and future climates considering new CMIP6 models

The susceptibility to climate change concerns the coffee market worldwide due to possible severe productivity losses. Brazil is the world's largest Arabica coffee producer and has crops in regions considered persistent climate change hotspots. Our study analyzed risks, vulnerabilities, and susceptibilities to pests and diseases in these regions under current and future climates and outlined adaptive measures to reduce future vulnerabilities. Ten risk indicators based on Arabica coffee requirements were proposed: water supply (Iw), base (TIB) and maximum temperature stresses (TImax), which delimit the temperature range where Arabica coffee grows and productivity is penalized outside both ranges, frost stress (TIfrost), diseases such as rust (DIrust), brown eye spot (DIbrown), and Phoma leaf spot (DIphoma), pests such as coffee berry borer (PIberry), coffee leaf miner (PIminer), and yield loss due to water stress (Iyg). Daily near-surface air temperature (minimum, mean, and maximum), relative humidity, precipitation, and global solar radiation were used from 16 General Circulation Models (GCMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), which are derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in three Shared Socioeconomic Pathways scenarios (SSP245, SSP370 and SSP585). All risk indicators were calculated for the current climate (1995–2014) and projected for the near (2041–2060), intermediate (2061–2080), and far future (2081–2100) in three SSPs and then classified into five risk classes (very low, low, moderate, high and very high). Our results indicated that due to increases in TImax and Iyg indicators, with high to very high risk in area and magnitude, Arabica coffee plantations will be negatively affected and economically unfeasible for about 35 % to 75 % of the studied area throughout the 21st century. Furthermore, the rust and the leaf miner will remain a concern in future climates due to increased temperatures and reduced relative humidity. The future of Arabica coffee crops in Brazil will depend on adopting effective adaptive measures and prudent agricultural strategies to address anticipated risks, including shifting crops to higher altitude areas, introducing more climate-resilient coffee cultivars/varieties, using agroforestry or intercropping systems, planting in closer spacing or higher density planting, and employing dripper or partial root-zone irrigation techniques.

Assessing future heat stress across China: combined effects of heat and relative humidity on mortality.

This study utilizes China's records of non-accidental mortality along with twenty-five simulations from the NASA Earth Exchange Global Daily Downscaled Projections to evaluate forthcoming heat stress and heat-related mortality across China across four distinct scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The findings demonstrate a projected escalation in the heat stress index (HSI) throughout China from 2031 to 2100. The most substantial increments compared to the baseline (1995-2014) are observed under SSP5-8.5, indicating a rise of 7.96°C by the year 2100, while under SSP1-2.6, the increase is relatively modest at 1.54°C. Disparities in HSI growth are evident among different subregions, with South China encountering the most significant elevation, whereas Northwest China exhibits the lowest increment. Projected future temperatures align closely with HSI patterns, while relative humidity is anticipated to decrease across the majority of areas. The study's projections indicate that China's heat-related mortality is poised to surpass present levels over the forthcoming decades, spanning a range from 215% to 380% from 2031 to 2100. Notably, higher emission scenarios correspond to heightened heat-related mortality. Additionally, the investigation delves into the respective contributions of humidity and temperature to shifts in heat-related mortality. At present, humidity exerts a greater impact on fluctuations in heat-related mortality within China and its subregions. However, with the projected increase in emissions and global warming, temperature is expected to assume a dominant role in shaping these outcomes. In summary, this study underscores the anticipated escalation of heat stress and heat-related mortality across China in the future. It highlights the imperative of emission reduction as a means to mitigate these risks and underscores the variances in susceptibility to heat stress across different regions.

Valuing mortality attributable to present and future temperature extremes in Argentina

This study analyzes the weather-related health damage of present and future extreme temperatures in Argentina. Focusing on mortality, short-term impacts of temperature are obtained by regressing monthly mortality rates on inter-annual monthly weather variability. For this purpose, a countrywide panel dataset at the municipal level was constructed from the universe of deaths between 2010 and 2019, and daily meteorological records from the ERA5 weather dataset. Then, NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) are used to project future mortality by 2085 under two climate scenarios. Finally, present and future mortality-related economic damages are assessed using the Value of a Statistical Life. The results show that one additional day of extreme temperatures increase all-cause mortality rates relative to mild weather and that the impact of hotter-than-average temperatures is greater in magnitude than that of colder ones. Substantial heterogeneity exists between causes of death and age groups, with older people facing greater risks, while the results for gender are inconclusive. All days of extreme cold in a year generate damage equivalent to 0.64% of GDP, while heat damage is 0.11% of GDP. The total damage by extreme temperatures adds up to 0.75% of the 2019 GDP. When future temperatures are valued, the total damage increases by an additional 1.45% under scenario RCP8.5 because the lower mortality occurring on cold days only partially offsets the increase in the number of hot days. On the contrary, if temperature changes were to be mild (i.e., under scenario RCP4.5), overall mortality would be lower at the national level and the corresponding damages would decrease by 0.02%.

Assessing the impacts of climate change on precipitation through a hybrid method of machine learning and discrete wavelet transform techniques, case study: Cork, Ireland

Study regionCork City, Ireland. Study focusReconstruction of precipitation timeseries is gaining increasing attention for monitoring and prediction studies. To address the challenges posed by non-smooth distributions of precipitation data, this research proposes an innovative reweighting method combining simulations from machine-learning and wavelet methods. By amalgamating multiple models, it aims to generate enhanced results, based on the concept that increasing the diversity of simulations and reweighting them improve the accuracy. New hydrological insights for the regionParticularly in regions like Cork City, which have experienced heavy precipitation events, accurate prediction is vital for informed decision-making. Here, monthly precipitation simulations from 35 National Aeronautics and Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) models of Phase 6 of the Climate Model Intercomparison Project (CMIP6) were utilized. The simulations considered the Shared Socioeconomic Pathways (SSPs): SSP1-2.6 and SSP5-8.5, for the hindcast and forecast periods of 1990–2014 and 2076–2100, respectively. Using the proposed technique, the results demonstrate the superior performance of the approach in reconstructing precipitation timeseries from NEX-GDDP-CMIP6 simulations, particularly in dealing with high precipitation intensities, while maintaining accuracy for low intensities. Additionally, the projections indicate that precipitation is expected to decrease from 122.88 during 1990–2014–115.67 (SSP1–2.6) and 118.71 mm/month (SSP5-8.5) in 2076–2100. The findings support the effectiveness of the hybrid method in improving data accuracy and reducing potential uncertainties in simulations.

What Does Global Land Climate Look Like at 2°C Warming?

AbstractConstraining an increase in global mean temperature below 2°C compared to pre‐industrial levels is critical to limiting dangerous and cascading impacts of anthropogenic climate change. Understanding future climatic changes and their spatial heterogeneity at 2°C warming is thus important for policy makers to prepare actionable adaptation and mitigation plans by identifying where and to what extent lives and livelihoods will be impacted. This study uses the recently released NASA Earth eXchange Global Daily Downscaled Projections (NEX‐GDDP) CMIP6 data to provide a broad overview of projected changes in six key climate variables and two climate impact indicators at a time when warming exceeds 2°C. Analysis of global mean temperature changes indicates the 2040s as the decade when most CMIP6 models reach 2°C warming with respect to a pre‐industrial period (1850–1900). During the 2040s, we find that global mean temperature, precipitation, relative humidity, downwelling shortwave and longwave radiation, and wind speed over land under the high emission scenario are projected to change by +2.8°C, +22.4 mm/year, −0.73%, −2.23 , +15.9 W/m2, and −0.04 m/s, respectively. Many of the future changes are expected to exacerbate climate impacts including heat stress and fire danger. Our analysis shows geographic patterns of policy‐relevant climatic changes, as parts of the globe will experience significant climate impacts even if the goal to keep warming below 2°C goal is achieved. Our results highlight the urgent need for further studies focused on identifying key hotspots and advancing region‐specific actionable adaptation and mitigation plans.

Projection of net irrigation requirement over India using a bias-corrected spatially downscaled dataset

Abstract With the country's economy directly dependent on food grain production, accurate estimation of the net irrigation requirement (NIR) of crops is essential for maximizing production. The daily IMD precipitation (0.25° × 0.25°) and temperature (1° × 1°) gridded datasets (1971–2010) were considered for the present scenario. Two satellite-based crop coefficient layers were prepared for the study: (i) kc_EEFlux, the evaporative fraction layer from earth engine evapotranspiration flux (EEFlux) and (ii) kc_FEWS, the ratio of the famine early warning system network (FEWSNET) monthly actual evapotranspiration (ET) (ETa) to ET0_FEWSNET layers. The NIR was estimated for three different cropping pixels derived from the land-use land cover (LULC) map of India, namely, Kharif-only, Rabi-only, and double/triple pixels using ‘ET0 and NIR toolbar’. The National Aeronautics and Space Administration (NASA)'s Earth Exchange Global Daily Downscaled Projections dataset were employed for projecting the future scenario. Results showed discrepancies in the extreme values of FAO56-Penman–Monteith (PM) and Hargreaves–Samani's estimated reference ET (ET0) but followed a similar trend in all months (R2 = 0.88). Results also suggested that FEWSNET offered more reliable estimates of NIR over EEFlux. The projected ensemble NIR indicated a probable negative relative change during the Kharif season (RCP4.5: from −14.47 to −7.25%; RCP8.5: from −11.45 to −5.59%) while a probable positive relative change during the Rabi season (RCP4.5: 3.30–5.21%; RCP8.5: 2.34–8.83%).

Predicting seedling development for two commercial forest species under current and future climates: A multi-model assessment

Development models are tools that couple environmental effects on development, allowing to predict development dynamics and to quantify the seedling phase duration for forest species under current and future climate conditions. Unfortunately, studies on forest species are scarce, even for commercially important species. This study calibrates and evaluates two development models under current climate conditions, and identifies possible changes in future climate conditions on the development of Corymbia citriodora (Hook.) (K.D. Hill & L.A.S. Johnson) and Eucalyptus urophylla (S.T. Blake), which are widely used in Brazilian forest plantations. The Phyllochron (Phyl) and Wang and Engel (WE) development models estimate the daily cumulative leaf number (CLN), and when integrated over time, gives the seedling phase duration (SPD). Field experiments were conducted using eleven sowing dates (SD) during 2014 and 2015 growing seasons in Itajubá, Minas Gerais, Brazil to evaluate the performance and calibrate the model coefficients for predicting the development of the two species. Subsequently, the best model was simulated for current climate conditions (1980–2005) and projected for future climates (2021–2050 and 2071–2100) under two radiative forcing scenarios (RCP 4.5 and 8.5) considering eleven SDs. Data from nine Earth System Models from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), that are derived from Coupled Model Intercomparison Project Phase 5 (CMIP5) were used. The Phyl and WE models were capable of predicting the CLN and SPD developmental dynamics for both species. However, the cumulative errors in CLN from the Phyl model, generated less accurate estimates for SPD. By contrast, the WE model was excellent (superior to the Phyl) at estimating CLN and SPD, with an error of less than 2.6 leaves, and 6 days, respectively. Additionally, the projected increases of up to 4 °C throughout the 21st century may modify the development rates, increase the SPD (∼+ 15 days), thus interfering with the permanence time of the two species at forest nursery, the date of sale, and seedling quality. Since the climate change time ranges exert greater influence on the SPD of both species, changing the sowing date and artificially controlling seedling production environments are the most viable adaptation measures to cope future climate change threats.

Assessment of Impacts of Climate Change on Indian Riverine Thermal Regimes Using Hybrid Deep Learning Methods

AbstractUnderstanding the riverine thermal regimes is challenging due to sparse spatiotemporal data of river water temperatures (RWTs). The development of systematic models combined with machine learning models under data‐limited context has not been intensively studied for the prediction of RWT. The present study developed hybrid models using long short‐term memory (LSTM), integrated with (a) k‐nearest neighbor (k‐NN) bootstrap resampling algorithms (kNN‐LSTM) to address the data limitations of RWT prediction and (b) discrete wavelet transform (WT) approach (WT‐LSTM) to address the time–frequency localized features of RWT prediction. The study assessed the climate change impacts on RWT using an ensemble of National Aeronautics Space Administration Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway scenarios 4.5 and 8.5 for seven major polluted river catchments of India. The hybrid kNN‐LSTM has effectively predicted RWT at monthly time scales under data limitations and outperformed the standalone LSTM, WT‐LSTM, and hybrid three‐parameter version air2stream models. The RWT increase for Tunga‐Bhadra, Musi, Ganga, and Narmada basins is predicted as 3.0, 4.0, 4.6, and 4.7°C, respectively, for 2071–2100.

Projected changes in precipitation characteristics over the Drakensberg Mountain Range

AbstractThis study examines the potential impacts of climate change on the characteristics of precipitation over the Drakensberg Mountain Range (DMR) at different global warming levels (GWLs: 1.5, 2.0, 2.5 and 3.0°C) under the Representative Concentration Pathway 8.5 (RCP8.5) scenario, using dynamical and statistical downscaled datasets. The dynamical datasets consist of 19 multi‐model simulations datasets from the Coordinated Regional Climate Downscaling Experiment (CORDEX), whereas the statistical downscaled datasets comprise 19 multi‐model simulations from the National Aeronautics and Space Administration (NASA) Earth Exchange (NEX) Global Daily Downscaled Projections (NEX‐GDDP, hereafter NEX). The capacity of the CORDEX and NEX datasets to represent past characteristics of extreme precipitation over the DMR was evaluated against eight observation datasets. The precipitation characteristics were represented by eight precipitation indices. Both CORDEX and NEX realistically capture the characteristics of extreme precipitation over the Drakensberg and, in most cases, their biases lie within the observation uncertainty. However, NEX performs better than CORDEX in reproducing most of the precipitation characteristics, except in simulating the threshold of extreme rainfall. The ensemble means of CORDEX and NEX agree on a future increase in the intensity of normal precipitation, in the frequency and intensity of extreme precipitation, as well as an increase in widespread extreme events, with a decrease in the number of precipitation days and continuous wet days. However, they disagree on the projected changes of annual precipitation, for which CORDEX projects an increase over most parts of the DMR, whereas NEX indicates a decrease. The self‐organizing‐map analysis, which reveals diversity in the projection patterns hidden in the ensemble means, shows the most probable combinations of projected changes in the annual precipitation and extreme precipitation events (in terms of intensity and frequency): (a) increase in both annual precipitation and extreme precipitation events; (b) decrease in both annual precipitation and extreme precipitation events; (c) decrease in annual precipitation but increase in extreme precipitation events. The results of this study can thus provide a basis for developing climate change adaptation and mitigating strategies over the DMR.

Observed and predicted trends in Icelandic snow conditions for the period 1930–2100

Abstract. This study presents an estimate of historical snow conditions in Iceland and a projection of these conditions, given different emission scenarios. Historical snow conditions were estimated using in situ observations from manned meteorological stations over the period 1930–2021 and by remotely sensed observations from the MODIS instruments over the period 2001–2021. Historical and future climate conditions, as described by each of the 21 general circulation models (GCM) from the 5th iteration of the Coupled Model Intercomparison Project (CMIP5) as contained in the NASA Earth Exchange (NEX) Global Daily Downscaled Projections (GDDP) dataset, were used to simulate snow conditions in Iceland over the period 1950–2100 under the Representative Concentration Pathways (RCP) RCP4.5 and RCP8.5 with the SNOW-17 model. The results show an increase in the average annual snow cover frequency (SCF) over the historical record detected both in the in situ (1930–2021) and remotely sensed data (2001–2021). Average annual snow depth measurements also revealed an increasing trend over the historical record. Simulated snow conditions show a substantial decrease in both snow water equivalent (SWE) and SCF over the period 1950–2100, a trend more pronounced under RCP8.5 as compared to RCP4.5.

Tracing seismic landslide-derived sediment dynamics in response to climate change

Mass movements such as landslides are prominent sources of sediment in post-seismic mountain belts. Earthquake triggered landslides can generate substantial volumes of fresh sediment and this may have significant long-term (decade to millennium) geomorphic effects on landscape evolution. Downslope and downstream transport of sediment into the channel network is sensitive to meteorological perturbations especially for extreme rainfall events. However, no established techniques can explicitly fingerprint/track landslide-derived sediment under climate change. In this study, we use a new tracing function incorporated into CAESAR-Lisflood (CL) to track the landslide-derived sediment dynamics in response to climate change. The hourly future rainfall sequences under two climate scenarios (RCP4.5, RCP8.5) were firstly generated using ‘NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP)’ dataset and then used as hydrological input to drive CL simulations. The landscape evolution at the Hongxi basin, which suffered great damage from the Wenchuan earthquake (Ms 8.0), was then simulated for 93 years (2008–2100) using CL under two climate scenarios. The results show that the landslides can greatly increase the sediment yield and climatic perturbations can lead to variation of sediment dynamics, the coupling of these two factors perform a combined impact on sediment budget, while the interplay and relevant importance between landslides and climate impact is dependent on the landslide density of specific basin. The landslide-channel connectivity (location of landslides with respect to river channels) plays an important role in post-earthquake sediment evacuation. The yearly sediment yield generated by the landslides can be stable after 20 years under both RCP 4.5 and RCP 8.5 scenarios in the study basin. These findings are of great importance to understand the interplay between landslide and climatic perturbations and support the risk management and mitigation after the 2008 Wenchuan earthquake.