Tag: Climate Change Downscaling Techniques

Abstract

Energy demand fluctuations due to low probability high impact (LPHI) micro-climatic events such as urban heat island effect (UHI) and heatwaves, pose significant challenges for urban infrastructure, particularly within urban built-clusters. Mapping short term load forecasting (STLF) of buildings in urban micro-climatic setting (UMS) is obscured by the complex interplay of surrounding morphology, micro-climate and inter-building energy dynamics. Conventional urban building energy modelling (UBEM) approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal scale. Reduced order modelling, unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization, limit the inter-building energy dynamics consideration into UBEMs. In addition, mismatch of resolutions of spatio–temporal datasets (meso to micro scale transition), LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of limitations. This review aims to direct attention towards an integrated-UBEM (i-UBEM) framework to capture the building load fluctuation over multi-scale spatio–temporal scenario. It highlights usage of emerging data-driven hybrid approaches, after systematically analysing developments and limitations of recent physical, data-driven artificial intelligence and machine learning (AI-ML) based modelling approaches. It also discusses the potential integration of google earth engine (GEE)-cloud computing platform in UBEM input organization step to (i) map the land surface temperature (LST) data (quantitative attribute implying LPHI event occurrence), (ii) manage and pre-process high-resolution spatio–temporal UBEM input-datasets. Further the potential of digital twin, central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizations is explored. It has also found that a trade-off between high-fidelity baseline simulation models and computationally efficient platform support or co-simulation platform integration is essential to capture LPHI induced inter-building energy dynamics.

Abstract

With an increasing focus on improving localized understanding of weather and climate phenomena and the computation cost involved in high-resolution modelling, spatial downscaling of data has proven to be a viable alternative method to obtain high-resolution climate data. Historically, several statistical and dynamical downscaling techniques have been employed to enhance coarse-resolution data to a finer grid scale. However, these conventional methods are either inefficient in capturing several finer scale details or are resource-heavy for recursive applications. Image Super-Resolution (SR) is a computer vision concept of using grid-based approaches to enhance the resolution of an image, analogous to spatial downscaling. In this study, we explore and compare the performance of different image super-resolution methods in downscaling the Indian Meteorological Department’s (IMD) gridded rainfall data from a low spatial resolution (1.0°) to a high resolution (0.25°). The SR methods considered include a fully connected autoencoder, four traditional convolutional neural networks, and two residual neural networks. The primary objective of this study was to make an initial assessment of the performance of such methods in downscaling gridded rainfall data over the Indian region. Furthermore, the resultant downscaled products have been compared using different objective metrics and analytical comparisons with ground truth data. One of the key findings of this study is that the residual learning-based neural networks demonstrated better performance in creating perceptually realistic rainfall maps (proven both quantitatively and qualitatively), closely followed by the traditional convolutional neural networks and the autoencoder.

Abstract

Agriculture and weather are intrinsically linked. Variations in the weather patterns due to climate change pose a foremost risk to agricultural production and food security. The IPCC (Intergovernmental Panel on Climate Change) propagates adaptation to tackle the irreversible climate change impact and its associated risks. The Hague Conference on Agriculture, Food Security, and Climate Change in 2010 gave the concept of climate-smart agriculture (CSA) as an adaptation measure to enhance food security by raising productivity, developing resilience systems to adjust to climate change, and dropping GHG (greenhouse gases) emissions. This study systematically reviews the literature using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) to understand the different practices followed by the farmers and the factors that determine the CSA adaptation. Most importantly, it examines the role of extension services in adaptation. The results show that the adapted practices among the different study areas can be broadly categorised into resilient technologies, conservation technologies, management technologies, diversification of income security, and risk mitigation strategies. The paper finds that the CSA adaption achieves the intended benefits with possible trade-offs and is determined through the socio-economic, institutional, behavioural factors and the land’s physical characteristics. The critical evaluation of different extension systems exhibits the importance of varying field schools to promote the CSAPs. The study also emphasises developing networks among the different stakeholders, particularly between formal extension and informal extensions such as NGOs (non-governmental organisations), farmer groups, and private players, and the inclusion of ICTs (information and communication technologies) for the holistic extension systems and effective delivery to the farmers’ CSA adaptation.

Abstract

The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions.

Abstract

High-resolution integration of large-scale and long-term anthropogenic nutrient discharge data is crucial for understanding the spatiotemporal evolution of pollution and identifying intervention points for pollution mitigation. Here, we establish the MEANS-ST1.0 dataset, which has a high spatiotemporal resolution and encompasses anthropogenic nutrient discharge data collected in China from 1980 to 2020. The dataset includes five components, namely, urban residential, rural residential, industrial, crop farming, and livestock farming, with a spatial resolution of 1 km and a temporal resolution of monthly. The data are available in three formats, namely, GeoTIFF, NetCDF and Excel, catering to GIS users, researchers and policymakers in various application scenarios, such as visualization and modelling. Additionally, rigorous quality control was performed on the dataset, and its reliability was confirmed through cross-scale validation and literature comparisons at the national and regional levels. These data offer valuable insights for further modelling the interactions between humans and the environment and the construction of a digital Earth.

Abstract

According to the IPCC, most regions worldwide will be gradually exposed to the amplification of the duration, frequency, and intensity of extreme climatic events, and the effects that extreme events can cause on human well-being and the economy. This study aims to develop linear regression models to estimate the soy and maize yields from extreme climatic and bioclimatic indices in three geographical subregions of Latin America (Mexico, Brazil, and Argentina) between 1979 and 2005. We used daily datasets from observations (CPC), reanalysis (ERA5), and regional climate model (RCM) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) to investigate the impact of extreme events of temperature and precipitation on maize and soy yields over the CORDEX Central America and South America domains. We first assessed the RCMs’ performance in reproducing extreme indices by comparing them against observations. The validation process evidenced the need for applying bias correction techniques to simulate daily precipitation and temperature for a better performance of the indices. The results show a higher correlation between the daily temperature range (DTR), cold nights and warm nights for soy production in Argentina (R2: − 0.74, − 0.80 and 0.75, respectively) and Mexico (R2: − 0.80, − 0.81, 0.70) for maize. Regionally, the linear model (simulated with observed data) using these indices presented an agreement with observed yield data in Mexico and Brazil, with explained variances exceeding 70% for maize in these subregions, while Argentina presented a better performance for soy yield. An intriguing finding was the superior performance of linear models when used with CPC-corrected RCM data compared to ERA5. Taken together, our results highlight the capabilities and constraints of linear models as valuable tools for developing adaptation and mitigation strategies, enabling precise yield forecasting, and informing policy decisions.

Abstract

The expansion of irrigated agriculture has increased global crop production but resulted in widespread stress on freshwater resources. Ensuring that increases in irrigated production occur only in places where water is relatively abundant is a key objective of sustainable agriculture and knowledge of how irrigated land has evolved is important for measuring progress towards water sustainability. Yet, a spatially detailed understanding of the evolution of the global area equipped for irrigation (AEI) is missing. In this study, we used the latest subnational irrigation statistics (covering 17,298 administrative units) from various official sources to develop a gridded (5 arcmin resolution) global product of AEI for the years 2000, 2005, 2010 and 2015. We found that AEI increased by 11% from 2000 (297 Mha) to 2015 (330 Mha), with areas of both substantial expansion, such as northwest India and northeast China, and decline, such as Russia. Combining these outputs with information on green (that is, rainfall) and blue (that is, surface and ground) water stress, we also examined to what extent irrigation has expanded unsustainably in places already experiencing water stress. We found that more than half (52%) of the irrigation expansion has taken place in areas that were already water-stressed in the year 2000, with India alone accounting for 36% of global unsustainable expansion. These findings provide new insights into the evolving patterns of global irrigation with important implications for global water sustainability and food security.

Abstract

Ensemble analysis is proven to provide advantages in climate change impact assessment based on outputs from climate models. Ensembled series are shown to outperform single-model assessments through increased consistency and stability. This study aims to test the improvement of precipitation estimates through the use of ensemble analysis for south and southwestern Turkey which is known to have complex climatic features due to varying topography and interacting climate forcings. The analysis covers an evaluation of the performance of eight regional climate models (RCMs) from the EUR-11 domain available from the CORDEX database. The historical outputs are evaluated for their representativeness of the current climate of the Mediterranean region and its surroundings in Turkey through a comparison with long-term monthly precipitation time series obtained from ground-based precipitation observations by the use of statistical performance indicators and Taylor diagrams. This is followed by a comparative evaluation of three ensemble methodologies, simple average of the models, multiple linear regression for superensemble, and artificial neural networks (ANN). The analysis results show that the overall performance of ensembled time series is better compared to individual RCMs. ANN generally provided the best performance when all RCMs are used as inputs. Improvement in the performance of ensembling due to the use of nonlinear models is further confirmed by fuzzy inference systems (FIS). Both ANN and FIS generated monthly precipitation time series with higher correlations with those of observations. However, extreme events are poorly represented in the ensembled time series, and this may result in inefficiency in the design of various water structures such as spillways and storm water drainage systems that are based on high return period events.

Abstract

Understanding how precipitation fluctuates geographically and temporally over a specific place due to climate change is critical. Generally, simulations of general circulation models (GCM) under different scenarios are downscaled to the local scale to study the impact of climate change on precipitation. However, selecting suitable GCMs for the given study area is one of the most hectic tasks, as the performance of GCMs may vary with respect to space and timescale. Therefore, the current study ranks twenty-seven CMIP 6 (Coupled Modelled Intercomparison Project Phase 6) GCMs in simulating precipitation over India for nine times series, including daily, monthly, yearly, and six extreme series extracted with annual maximum and peak over threshold methods. The gridded daily rainfall data provided by the India Meteorological Department (IMD) are used as the observed data. The GCMs' outputs are corrected for the systematic bias using the linear scaling method. The performance of a GCM is assessed with three statistical performance metrics, namely NSE, RMSE, and R². The GCMs' ranks are determined using a multi-criterion decision-making technique named the modified technique of order preference by similarity to an ideal solution (mTOPSIS) for every grid point and nine timescales (i.e., daily, monthly, yearly, and six extreme series). From the results, for the entire India, the top ten recommended CMIP 6 GCMs are FGOALS-g3, HadGEM3-GC31-MM, EC-Earth3, BCC-CSM2-MR, CNRM-CM6-1-HR, CanESM5, AWI-ESM-1-1-LR, MPI-ESM-1-2-HR, IITM-ESM, and INM-CM5-0. The identified best-performing models provide insightful information for better regional climate projections and underscore the necessity of considering multiple model outputs for reliable climate change impact assessments and adaptation strategies in the region.

Abstract

Climate change is a pressing global issue that has far-reaching effects on the environment, including its impact on infrastructure such as roads and pavements. As temperatures rise, it is essential to consider selecting asphalt binder performance grades to ensure the long-lasting and reliable performance of asphalt pavements. In Sharjah, the penetration grading system guides the selection of asphalt binder grade (grade 60/70). However, this grading system is empirical in nature and doesn’t take into account local temperature fluctuations. The superpave performance grade (PG) system considers explicitly local temperature records while allowing the incorporation of future temperature changes. The main goal of this study project is to assess the impact of future temperature changes on the required asphalt binder PG in Sharjah and the consequent impact on pavement performance simulated by AASHTOWare Pavement ME Design. Future temperature records were predicted via the MRI-CGCM3 model considering two RCPs (4.5 and 8.5) and two future periods (near and far). The historical temperature records revealed a PG 70–10 at a 98% reliability level for Sharjah compared to a PG 64–16, equivalent to the currently used 60/70 penetration grade. Future temperature changes yielded a higher performance grade of PG 76–10 for the far future, considering RCP 8.5. The performance simulations emphasized the importance of carefully selecting the asphalt binder PG, as this decision has been shown to increase the life span of asphalt pavement.