Tag: Climate Change Downscaling Techniques

Abstract

This study proposes a novel fusion framework for flood forecasting based on machine-learning, statistical, and geostatistical models for daily multiple-step-ahead and near-future under climate change scenarios. An efficient machine-learning model with three remote-sensing precipitation products, including ERA5, CHIRPS, and PERSIANN-CDR, was applied to gap-fill data. Four individual machine-learning models, including Random Forest, Multiple-Layer Perceptron, Support Vector Machine, and Extreme Learning Machine, were developed twelve days ahead of streamflow modeling. Then, three fusion models, including Random Forest, Bayesian Model Averaging, and Bayesian Maximum Entropy, were applied to combine the outputs of individual machine-learning models. The proposed framework was also implemented to downscale the precipitation variables of three general climate models (GCMs) under SSP5-8.5 and SSP1-2.6 scenarios. The application of this approach is investigated on the Kan River, Iran. The results indicated that individual models illustrated weak performance, especially in far-step-ahead flood forecasting, so it is necessary to utilize a fusion technique to improve the results. The RF model indicated high efficiency in the fusion step compared to other fusion-based models. This technique also demonstrated effective proficiency in downscaling daily precipitation data of GCMs. Finally, the flood forecasting model was developed based on the fusion framework in the near future (2020–2040) by using precipitation data from two scenarios. We conclude that flood events based on SSP5-8.5 and SSP1-2.6 will increase in the future in our case study. Also, the frequency evaluation shows that floods under SSP1-2.6 will occur about 10% more than SSP5-8.5 in the Kan River basin from 2020 to 2040.

Abstract

Rising temperatures due to global warming necessitate immediate evaluation of heatwave patterns in Peninsular Malaysia (PM). For this purpose, this study utilized a locally developed heatwave index and a gridded daily maximum temperature (Tmax) dataset from ERA5 (1950–2022). During validation, the ERA5 dataset accurately represented the spatial pattern of Level 1 heatwaves, showing widespread occurrence. Historically, Level 1 heatwaves prevailed at 63.0%, followed by Level 2 at 27.7%, concentrated in northwestern states and the enclave between the Tahan and Titiwangsa mountain ranges. During very strong El Niño events in 1982/83, 1997/98, and 2015/16, Level 2 heatwave distributions were 10.4%, 26.8%, and 15.0%, respectively. For future projection, the model ensemble was created by selecting top-performing Global Climate Models (GCMs) using Kling-Gupta efficiency (KGE), ranked re-aggregation with compromise programming index (CPI), and GCM subset selection via Fisher-Jenks. The linear scaling bias-corrected GCMs (BC-GCMs), NorESM2-LM, ACCESS-CM2, MPI-ESM1-2-LR, ACCESS-ESM1-5, and FGOALS-g3, were found to exhibit better performance, and then ensemble. March to May show the highest increase in all scenarios, ranging from 3.3 °C to 4.4 °C for Level 1 heatwaves and 4.1 °C to 10.7 °C for Level 2 heatwaves. In the near future, SSP5-8.5 projects up to a 40.5% spatial increase for Level 1 heatwaves and a 2.3% increase for Level 2 heatwaves, affecting 97.1% and 57.2% of the area, respectively. In the far future, under SSP2-4.5 and SSP5-8.5, Tmax is projected to rise rapidly (1.5–4.5 °C) in the northern, western, and central regions, with increasing population exposure anticipated in the northern and western regions.

Abstract

The global energy demand has greatly impacted greenhouse gas emissions and climate change. Since buildings are responsible for a large portion of global energy consumption, this study investigates the energy-saving potential of green roofs and cool roofs in reducing building energy consumption. Using an integrated approach that combines climate change modeling and building energy simulation, the study evaluates these strategies in six global cities (Cairo, Hong Kong, Seoul, London, Los Angeles, and Sao Paulo) under current and future climate change scenarios. The results show that in future climates, the implementation of green and cool roofs at the city level can lead to substantial annual energy reductions, with up to 65.51% and 71.72% reduction in HVAC consumption, respectively, by 2100. These findings can guide the implementation of these strategies in different climatic zones worldwide, informing the selection and design of suitable roof mitigation strategies for specific urban contexts.

Abstract

The accuracy of the Weather Research and Forecasting Model (WRF) can be affected by multiple factors, including domain resolution, geostatic data, and model configuration. This study examined the sensitivity of simulated seasonal temperature to different geostatic data, horizontal domain resolutions, and configurations in Northeast Iran. During the investigation, the WRF model utilized Asymmetric Convective Model version 2 (ACM2) planetary boundary layer, WRF-single-moment-microphysics classes 6 (WSM6) Microphysic, Geophysical Fluid Dynamics Laboratory (GFDL) Long-wave/short-wave radiation parameterization schemes, and Climate Forecast System version2 (CFSV2) initial and boundary conditions from Nov 2019 to Feb 2020. The default (States Geological Survey (USGS)/ Moderate Resolution Imaging Spectroradiometer (MODIS)) and high-resolution (ASTER/Copernicus) geostatic data and inner domain resolutions 3 and 6 km were set for model simulation. The results revealed that following the physical configuration, the model simulation’s highest sensitivity was associated with the domain resolution and geostatic data. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) had approximately similar results in the 6 km domain for both geostatic data, but the Mean Bias (MB) showed a cold Bias. The MB results were warmer when the horizontal resolution increased from 6 to 3 km. To obtain reliable temperature simulation, WRF was more sensitive to horizontal domain resolution than geostatic data. However, the accuracy of geostatic data affected the distribution of temperature patterns. A greater error appeared in the lower horizontal domain resolution (6 km) and low-resolution geostatic data (default), especially in complex terrains.

Abstract

The accuracy of the Weather Research and Forecasting Model (WRF) can be affected by multiple factors, including domain resolution, geostatic data, and model configuration. This study examined the sensitivity of simulated seasonal temperature to different geostatic data, horizontal domain resolutions, and configurations in Northeast Iran. During the investigation, the WRF model utilized Asymmetric Convective Model version 2 (ACM2) planetary boundary layer, WRF-single-moment-microphysics classes 6 (WSM6) Microphysic, Geophysical Fluid Dynamics Laboratory (GFDL) Long-wave/short-wave radiation parameterization schemes, and Climate Forecast System version2 (CFSV2) initial and boundary conditions from Nov 2019 to Feb 2020. The default (States Geological Survey (USGS)/ Moderate Resolution Imaging Spectroradiometer (MODIS)) and high-resolution (ASTER/Copernicus) geostatic data and inner domain resolutions 3 and 6 km were set for model simulation. The results revealed that following the physical configuration, the model simulation’s highest sensitivity was associated with the domain resolution and geostatic data. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) had approximately similar results in the 6 km domain for both geostatic data, but the Mean Bias (MB) showed a cold Bias. The MB results were warmer when the horizontal resolution increased from 6 to 3 km. To obtain reliable temperature simulation, WRF was more sensitive to horizontal domain resolution than geostatic data. However, the accuracy of geostatic data affected the distribution of temperature patterns. A greater error appeared in the lower horizontal domain resolution (6 km) and low-resolution geostatic data (default), especially in complex terrains.

Abstract

The complex topography and inherent nonlinearity affiliated with influential hydrological processes of urban catchments, coupled with limited availability of measured data, limits the prediction accuracy of conventional models. Artificial Neural Network models (ANNs) have displayed commendable progress in recognising and simulating highly complex, non-linear associations allied with input-output variables, with limited comprehension of the underlying physical processes. Therefore, this paper investigates the effectiveness and accuracy of ANN models, in estimating the urban catchment runoff, employing minimal and commonly available hydrological data variables – rainfall and upstream catchment flow data, employing two powerful supervised-learning-algorithms, Bayesian-Regularization (BR) and Levenberg-Marquardt (LM). Gardiners Creek catchment, encompassed in Melbourne, Australia, with more than thirty years of quality-checked rainfall and streamflow data was chosen as the study location. Two significant storm events that transpired within the last fifteen years - the 4th of February 2011 and the 6th of November 2018, were nominated for calibration and validation of the ANN model. The study results advocate that the use of the LM-ANN model stipulates accurate estimates of the historical storm events, with a stronger correlation and lower generalisation error, in contrast to the BR-ANN model, while the integration of upstream catchment flow alongside rainfall, vindicate for their collective impact upon the dynamics of the flow being spawned at the downstream catchment locations, significantly enhancing the model performance and providing a more cost-effective and near-realistic modelling approach that can be considered for application in studies of urban catchment responses, with limited data availability.

Abstract

The complex topography and inherent nonlinearity affiliated with influential hydrological processes of urban catchments, coupled with limited availability of measured data, limits the prediction accuracy of conventional models. Artificial Neural Network models (ANNs) have displayed commendable progress in recognising and simulating highly complex, non-linear associations allied with input-output variables, with limited comprehension of the underlying physical processes. Therefore, this paper investigates the effectiveness and accuracy of ANN models, in estimating the urban catchment runoff, employing minimal and commonly available hydrological data variables – rainfall and upstream catchment flow data, employing two powerful supervised-learning-algorithms, Bayesian-Regularization (BR) and Levenberg-Marquardt (LM). Gardiners Creek catchment, encompassed in Melbourne, Australia, with more than thirty years of quality-checked rainfall and streamflow data was chosen as the study location. Two significant storm events that transpired within the last fifteen years - the 4th of February 2011 and the 6th of November 2018, were nominated for calibration and validation of the ANN model. The study results advocate that the use of the LM-ANN model stipulates accurate estimates of the historical storm events, with a stronger correlation and lower generalisation error, in contrast to the BR-ANN model, while the integration of upstream catchment flow alongside rainfall, vindicate for their collective impact upon the dynamics of the flow being spawned at the downstream catchment locations, significantly enhancing the model performance and providing a more cost-effective and near-realistic modelling approach that can be considered for application in studies of urban catchment responses, with limited data availability.

Abstract

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry, it has become imperative to monitor and mitigate these threats to ensure civil aviation safety. The eddy dissipation rate (EDR) has been established as the standard metric for quantifying turbulence in civil aviation. This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder (QAR) data. The detection of atmospheric turbulence is approached as an anomaly detection problem. Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events. Moreover, comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available. In summary, the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data, comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms. This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Abstract

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry, it has become imperative to monitor and mitigate these threats to ensure civil aviation safety. The eddy dissipation rate (EDR) has been established as the standard metric for quantifying turbulence in civil aviation. This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder (QAR) data. The detection of atmospheric turbulence is approached as an anomaly detection problem. Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events. Moreover, comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available. In summary, the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data, comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms. This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

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.