Tag: Climate Change Downscaling Techniques

Hemispheric asymmetric response of tropical cyclones to CO2 emission reduction

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Abstract

Tropical cyclones (TCs) are among the most devastating natural hazards for coastal regions, and their response to human activities has broad socio-economic relevance. So far, how TC responds to climate change mitigation remains unknown, complicating the design of adaptation policies. Using net-zero and negative carbon emission experiments, we reveal a robust hemisphere-asymmetric hysteretic TC response to CO2 reduction. During the decarbonization phase, the Northern Hemisphere TC frequency continues to decrease for several more decades, while the Southern Hemisphere oceans abruptly shifts to a stormier state, with the timescales depending on mitigation details. Such systematic changes are largely attributed to the planetary-scale reorganization of vertical wind shear and midlevel upward motion associated with the hysteretic southward migration of the Intertropical Convergence Zone, underpinned by the Atlantic Meridional Overturning Circulation and El Niño-like mean state changes. The hemispheric contrast in TC response suggests promising benefits for most of the world’s population from human action to mitigate greenhouse gas warming, but it may also exacerbate regional socioeconomic disparities, for example by putting more pressure on small open-ocean island states in the Southern Hemisphere to adapt to TC risks.

Impact of Initial Soil Conditions on Soil Hydrothermal and Surface Energy Fluxes in the Permafrost Region of the Tibetan Plateau

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Abstract

Accurate initial soil conditions play a crucial role in simulating soil hydrothermal and surface energy fluxes in land surface process modeling. This study emphasized the influence of the initial soil temperature (ST) and soil moisture (SM) conditions on a land surface energy and water simulation in the permafrost region in the Tibetan Plateau (TP) using the Community Land Model version 5.0 (CLM5.0). The results indicate that the default initial schemes for ST and SM in CLM5.0 were simplistic, and inaccurately represented the soil characteristics of permafrost in the TP which led to underestimating ST during the freezing period while overestimating ST and underestimating SLW during the thawing period at the XDT site. Applying the long-term spin-up method to obtain initial soil conditions has only led to limited improvement in simulating soil hydrothermal and surface energy fluxes. The modified initial soil schemes proposed in this study comprehensively incorporate the characteristics of permafrost, which coexists with soil liquid water (SLW), and soil ice (SI) when the ST is below freezing temperature, effectively enhancing the accuracy of the simulated soil hydrothermal and surface energy fluxes. Consequently, the modified initial soil schemes greatly improved upon the results achieved through the long-term spin-up method. Three modified initial soil schemes experiments resulted in a 64%, 88%, and 77% reduction in the average mean bias error (MBE) of ST, and a 13%, 21%, and 19% reduction in the average root-mean-square error (RMSE) of SLW compared to the default simulation results. Also, the average MBE of net radiation was reduced by 7%, 22%, and 21%.

A systematic review for assessing the impact of climate change on landslides: research gaps and directions for future research

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Abstract

The magnitude and intensity of landslides due to changing climate have created environmental and socio-economic implications for society. Through an in-depth analysis of the existing research on landslides in a changing climate from 1996 to 2021, this paper aims to carry out bibliometric and thematic analyses, identify the research gaps in the existing literature, and suggest a future framework for climate change-induced landslide risk assessment and mitigation. The data for review was collected from the Web of Science and Scopus platforms using a set of relevant keywords. After meeting the exclusion and inclusion criteria, 200 studies were finally selected to analyze the current state of research. The findings revealed that most of the reviewed studies focused on economic vulnerability to landslides, while social and ecological aspects of vulnerability at the micro-scale were scant in the past literature. Uncertainty in landslide-climate modeling, lack of advanced models for predicting landslide risk, and lack of early warning systems were identified as the major research gaps. A holistic methodological approach is proposed for assessing landslide risk and devising landslide mitigation strategies. The identified research gaps and the proposed framework may help in the future progression of climate change-induced landslide research in spatial information science.

Comparative evaluation of performances of algae indices, pixel- and object-based machine learning algorithms in mapping floating algal blooms using Sentinel-2 imagery

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Abstract

One of the main threats to freshwater resources is pollution from anthropogenic activities such as rapid urbanization and excessive agricultural nutrient runoff. Remote sensing technologies have been effectively used in monitoring and mapping rapid changes in the marine environment and assessing the overall health of freshwater ecosystems. The main goal of this study is to comparatively evaluate the performance of index-based and classification-based approaches in mapping dense floating algal blooms observed in Lake Burdur using Sentinel-2 imagery. For index-based mapping, algae-specific indices, namely the Floating Algae Index (FAI), Adjusted Floating Algae Index, Surface Algal Blooms Index (SABI), and Algal Blooms Detection Index (ABDI), were used. At the same time, pixel- and object-based Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory Network (LSTM) were utilized for classification-based algal mapping. For this purpose, seven Sentinel-2 images, selected through time series analysis performed on the Google Earth Engine platform, were used as the primary dataset in the application. The results show that high-density floating algae formations can be detected over 99% by both indices and classification-based approaches, whereas pixel-based classification is more successful in mapping low-density algal blooms. When two-class thematic maps representing water and floating algae classes were considered, the maps produced by index-based FAI using an appropriate threshold value and the classification-based RF algorithm reached an overall accuracy of over 99%. The highest algae density in the lake was observed on July 13, 2021, and was determined to be effective in ~ 45 km2 of the lake’s surface.

Climate change projections in Guatemala: temperature and precipitation changes according to CMIP6 models

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Abstract

Projected changes in precipitation and temperature for Guatemala were examined using the phase 6 dataset of the Coupled Model Intercomparison Project (CMIP6). CMIP6 models project alterations in annual mean temperature and precipitation in Guatemala relative to the current climate. A set of 25 CMIP6 models project a continuous increase in annual mean temperature over Guatemala during the twenty-first century under four future scenarios. The data provided by WorldClim has a spatial resolution of 2.5 min (of a longitude/latitude degree) this means a 4.5 km × 4.5 km of area of each pixel approximately. for the climate horizons of 2021–2040, 2041–2060, 2061–2080, and 2081–2100, these were adjusted based on the average of 38 local stations in Guatemala from the period (1970–2000). The projected temperature shows a large increase over 5 °C under the SSP5-8.5 scenario, over the northern parts of Guatemala and the northwest. By the end of the twenty-first century, the annual mean temperature in Guatemala is projected to increase by on average 1.8 °C, 2.9 °C, 4.3 °C, and 5.4 °C under the SSP1_2.6, SSP2_4.5, SSP3_7.0, and SSP5_8.5 scenarios, respectively, relative to current climate (1990–2020). The warming is differentiated on a monthly time scale, with CMIP6 models projecting greater warming in July, August, and September, part of the summer and autumn season. Annual precipitation is projected to decrease in Guatemala during the twenty-first century under all scenarios. The rate of change in projected mean annual precipitation varies considerably among scenarios; − 5%, − 9%, − 18%, and − 22% under the SSP1_2.6, SSP2_4.5, SSP3_7.0, and SSP5_8.5 scenarios, respectively. Monthly precipitation projections show great variability, with projected precipitation for the months of May, June, and July, part of the spring and summer, showing a greater decrease than other months and specifically in the northern part of the country. On the other hand, mid-summer precipitation (July and August) shows a decrease in the central and eastern part of the country. The results presented in this study provide baseline information on CMIP6 models for Guatemala, which serve as a basis for developing climate change adaptation and mitigation strategies.

Downscaling and reconstruction of high-resolution gridded rainfall data over India using deep learning-based generative adversarial network

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Abstract

To expedite regional-scale climate change impact research and assessments, the downscaling of climate data is a crucial prerequisite. Image super-resolution, which is analogous to gridded data downscaling, is the concept of improving the pixel quality of images using deep learning techniques. In this study, the performance of a Super-Resolution Generative Adversarial Network (SRGAN), a cutting-edge deep learning-based image super-resolution technique, is assessed in producing perceptually realistic high-resolution rainfall data over India from the low-resolution input. The main component of SRGAN is a generator network that takes abstract information from low-resolution (LR) rainfall data to infer potential high-resolution (HR) counterparts. A Super-Resolution Residual Neural Network (SRResNet) is used as the generator network. It is trained using a supervised learning strategy (SRResNet) and adversarial learning strategy (several variants created, e.g., SRGAN-MSE, SRGAN-VGGB2, SRGAN-VGGB3 and SRGAN-VGGB4). A statistical downscaling method called bias correction and spatial disaggregation (BCSD) is also employed to compare with the deep learning-based downscaling methods. All these methods are rigorously assessed for their ability to reconstruct distribution, mean, and extreme rainfall during the test period. Our results show that the supervised learning-based SRResNet and adversarial learning-based SRGAN-MSE variant has an upper hand over the BCSD method for gridded rainfall downscaling. These findings have important implications for enhancing the precision and quality of regional climate data in the context of climate change impact assessment.

Performance evaluation of a high-resolution regional climate model in West Africa: sensitivity to land surface schemes

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This research examines the suitability of four land surface schemes (LSSs) in Weather Research and Forecasting (WRF) model over different West Africa’s (WA) climatological zones, namely Guinea, Savanna, and Sahel. The four LSSs include Noah, Noah (Multiparameterization) MP, Noah MP with the groundwater option (Noah GW), and Community Land Model Version 4 (CLM4). A 3-month simulation was carried out with each LSS during July–September 2012. Temperature and dew point temperature (Dpt) were evaluated using the ERA5 dataset, while precipitation was evaluated using the TRMM product. Based on the three variables, CLM4 is most suitable for the Guinea zone; Noah, Noah MP, and CLM4 perform equally well for Savanna; and Noah MP is most suitable for the Sahel zone. In general, and over all zones, Noah MP is most suitable for precipitation simulation, while CLM4 is the best for dew point temperature as Noah MP and Noah GW are equally suitable for 2 m temperature. Noah GW overestimates surface moisture, altering surface fluxes, increasing evaporative fraction, and increasing convective activities (especially in semi-arid areas), which led to a significant bias in temperature and precipitation. Also, over the Savanna and Sahel zone, a strong African Easterly Jet (AEJ) with a weak Tropical Easterly Jet (TEJ) in Noah led to lower rainfall. In contrast, weak AEJ with strong TEJ in Noah MP and Noah GW caused higher rainfall. Further studies would be to evaluate the sensitivity of each LSS under different initial conditions, and their land-atmosphere interactions strength over the zones.

Simulation of extreme precipitation changes in Central Asia using CMIP6 under different climate scenarios

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Central Asia has a dry climate, scarce water resources, extremely fragile ecosystems, and frequent extreme precipitation events. Using the data of 22 global climate models in the CMIP6 plan, the trend of the extreme precipitation index under four Shared Socioeconomic Pathways (SSPs) was estimated by calculating eight extreme precipitation indices in Central Asia and optimizing the best multi-model set using the Taylor evaluation and comprehensive score. The results showed that in Central Asia, the CMIP6 mode and multi-mode collection can reasonably reproduce the regional differences of various severe precipitation indices. However, these results only performed well for consecutive dry days (CDD) and annual total precipitation (PRCPTOT), but poorly for the replication of extreme high- and low-value regions. We found that the simulation effect of the multi-mode ensemble results was better than that of a single mode, and that CMIP6 can roughly depict the evolving characteristics of extreme precipitation events. However, the CMIP6 data performed poorly in terms of spatial divergence ability characteristics. According to the estimated results, mountainous regions have experienced considerable changes, and a significant increase in the range of change was observed for severe precipitation (consecutive wet days (CWD), single day maximum precipitation (Rx1day), and PRCPTOT) in wet and dry regions during the twenty-first century. Simultaneously, the humidification trend accelerated after 2050, and four shared socioeconomic paths showed similar trends; however, the extreme precipitation rate was higher under the high forcing path. Consecutive dry days (CDD) in Central Asia decreased by 90% under SSP5-8.5 relative to SSP1-2.6, whereas CWD, Rx1day, and PRCPTOT increased by 20%, 150%, and 118%, respectively.

Projected irrigation demand for large-scale rice granary under future climate scenarios based on CMIP6 multi-GCM ensemble: a case study of Kerian Irrigation Scheme, Malaysia

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Future climate prediction at a local scale is one of the pressing challenges affecting water management-related mitigation plans. The rice irrigation demands are always related to the climate of the area. This study presents possible changes in the monthly rice irrigation demand patterns under future climate scenarios in the Kerian Irrigation Scheme, Malaysia. An ensemble of five Global Climate Models under three Shared Socioeconomic Pathways (SSPs) (SSP1-2.6, SSP2-4.5, and SSP5-8.5) was employed to help project irrigation demand from 2021 to 2080. The study compared the future projections with the baseline period (1985–2014) and revealed that future irrigation demand changes for two planting periods range between − 1.0 to 0.1% and − 5.3 to − 2.6% during the dry season (February–July) and wet season (August–January), respectively. A significant decrease in irrigation water demand was predicted in September and October for each SSP scenario due to increased rainfall during the wet season, with SSP5-8.5 being the most prominent. Although the temperature and reference evapotranspiratopn (ETo) were predicted to increase, mainly during the near future (2021–2050) rather than the far future (2051–2080), the increase in predicted monthly rainfall successfully copes with the risk of the possible high demand for irrigation supply. Climate change potentially alters the future monthly irrigation water demand pattern, resulting in challenges to water resource management. Predicting the impacts of rice irrigation water demand under the potential future climate change is crucial for Bukit Merah Reservoir to help establish appropriate operational policies for irrigation release for its sustainability.

Climate change impacts on the Nahavand karstic springs using the data mining techniques

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Karst resources are sensitive to environmental changes, especially climate change. In this research, monthly data (years 1994–2020) were collected for five karstic springs, namely Famaseb (Sp1), Faresban (Sp2), Ghalebaroodab (Sp3), Giyan (Sp4), and Gonbadkabood (Sp5), located in the Nahavand plain, west of Iran. Data mining models such as KNN, SVM, and M5tree were used to simulate the discharge of springs. The results obtained, based on two statistical indices, correlation coefficient (r) and normalized root mean square error (nRMSE), the M5tree model was more accurate than the other models and was selected to simulate the discharge of the springs. The r-value was equal to 0.736, and nRMSE was 0.113. In the later stage, discharge of the springs was projected for four time periods: 2021–2040, 2041–2060, 2061–2080, and 2081–2100 under two scenarios, RCP8.5 and RCP4.5. The results showed that in the monthly survey, the ratio of the variation in the discharge of the springs compared to the historical period in the Sp1 and Sp2 springs had the most variations. It was found that in November, there was an average increase of + 250% in the investigated periods. Also, the highest decrease compared to the historical period of these two springs was observed during May, which were about − 37 and − 70%, respectively. In springs Sp3, Sp4, and Sp5, like the previous two springs, the most fluctuations occurred in November and May, respectively.

Graphical abstract