Tag: Climate Change Downscaling Techniques

Abstract

Projected changes in atmospheric blocking and associated extreme weather are marked by considerable uncertainties. While paleoclimate records could help reduce these uncertainties, their low temporal resolution makes extracting synoptic-scale signals challenging. Here, a deep learning model is developed to infer summertime blocking frequency from tree-ring-based gridded reconstructions of Northern Hemisphere surface temperature over the Last Millennium. The model, despite not directly incorporating paleoclimate proxies or their locations, is implicitly constrained by them. The reconstructions highlight the tropical Pacific’s strong influence on blocking variability at interannual-to-centennial time scales. A weakened tropical Pacific zonal temperature gradient during the Little Ice Age correlates with a hemispherically reduced -yet more variable interannually- blocking frequency and altered regional patterns. This deep learning approach offers a pathway for extracting paleoweather signals from paleoclimate records that enables improved understanding of blocking response to external forcing and constraining of model projections of blocking under climate change scenarios.

Abstract

Unmanned aerial vehicle (UAV) imaging methods have drawn a lot of interest lately from academics and industry professionals as an affordable option for agro-environmental uses. To improve the UAV capabilities of diverse applications, machine learning (ML) methods are specifically applied to UAV-based remote sensing data. Spatiotemporal properties were analysed and the city-level carbon emissions statistics were estimated. This research proposes novel technique in UAV-based climate temperature pattern analysis and carbon emission detection utilizing the deep learning (DL) model. Here the input is collected as UAV-based weather data which is processed for noise removal and smoothening. Processed data is extracted and classified utilizing Gaussian belief deep neural network and spatial convolutional Q-swarm colony metaheuristic optimization. A metaheuristic optimisation algorithm uses gradient information-free iterative evaluation of the objective function in order to identify a global optimum. Experimental analysis has been carried out in terms of detection accuracy, average precision, F-1 score, recall and AUC for various UAV-based weather dataset. The proposed technique attained mean average precision was 92%, recall was 94%, AUC was 87%, detection accuracy was 96% and F1-score was 93%. This work shows that remotely sensed data can be used to support more advanced evaluations that are more successful, particularly in areas with extensive selective logging and diverse forest conditions, and to help quantify carbon emissions from selective logging using conventional methodologies.

Abstract

Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty of GCM-based precipitation projections in large-scale watersheds. The Pearl River Basin (PRB) in southern China was selected as the study area to validate the reliability of this framework. Spatially, we investigated the features of terrain-related spatial heterogeneity in precipitation simulation of different climate models using a stepwise cluster zoning approach. The spatial performance of most CMIP6 models was effective in capturing the annual mean precipitation from the source region to the downstream of the PRB. To further evaluate the model's skill in simulating precipitation patterns, we conducted a seasonal analysis for different periods throughout the year. However, the seasonal precipitation cycle exhibited a wet bias during cold seasons, and the most significant deviation of precipitation percentage intervals occurred during winter. The TSS ranking of CMIP6 models was used to select the top-performing models to construct an improved multi-model ensemble mean (MEM5), resulting in a more accurate precipitation simulation for PRB. Results showed consistent precipitation increases (p < 0.05) for all scenarios in the PRB, with the middle and lower reaches being the most sensitive to changes in precipitation. The improved MEM5 can serve as a valuable reference for accurately simulating hydrological regimes and extreme weather events in the PRB. The proposed multi-model ensemble downscaling framework, which incorporates SCA, offers a new approach for high-resolution and low-uncertainty climate simulations in other large-scale watersheds.

Abstract

Located in the climatically vulnerable Middle East, Iraq is projected to be highly susceptible to climate change impacts. Evaluating these effects on water resources requires an analysis of key hydrological factors, including rainfall and associated runoff. The Soil Conservation Service Curve Number (SCS-CN) method remains the foremost runoff modelling approach because it relies on the curve number (CN), derived from land use, soil type, and other parameters. In this chapter, the SCS-CN model is integrated with geographic information systems (GIS) and Long Aston Research Station Weather Generation (LARS-WG) to estimate rainfall-runoff under climate change projections for the Kirkuk governorate in Iraq. The SCS-CN model inputs of hydrological soil properties, rainfall data, and potential retention were obtained through GIS analysis. The results from 1992 to 2022 showed an average annual runoff of a maximum runoff of 290 mm in 1992 and a minimum of 184 mm in 2021. Under future climate change projections, the estimated annual average runoff is predicted to decline to 110 mm by 2055, with a maximum of 280 mm by 2025. These findings indicate substantial climate change impacts on future rainfall, the associated surface runoff, water availability, and water resources in the Kirkuk governorate. Integrating established hydrologic models, such as SCS-CN with GIS, can enhance climate change impact assessments for improved water resource planning.

Abstract

Recent development of artificial intelligence (AI) technology has resulted in the fruition of machine learning-based weather prediction (MLWP) systems. Five prominent global MLWP model, Pangu-Weather, FourCastNet v2 (FCN2), GraphCast, FuXi, and FengWu, emerged. This study conducts a homogeneous comparison of these models utilizing identical initial conditions from ERA5. The performance is evaluated in the Eastern Asia and Western Pacific from June to November 2023. The evaluation comprises Root Mean Square Error and Anomaly Correlation Coefficients within the designated region, typhoon track and intensity predictions, and a case study for Typhoon Haikui. Results indicate that FengWu emerges as the best-performing model, followed by FuXi and GraphCast, with FCN2 and Pangu-Weather ranking lower. A multi-model ensemble, constructed by averaging predictions from the five models, demonstrates superior performance, rivaling that of FengWu. For the 11 typhoons in 2023, FengWu demonstrates the most accurate track prediction; however, it also has the largest intensity errors.

Abstract

The stability of environmental conditions around the world has been threatened by climate change in the last few decades. Sea levels are rising more quickly than ever before due to a combination of factors including temperature increases, changes in precipitation patterns, and glacier melting. We describe a machine learning strategy to design coastal sea level fluctuation as well as related uncertainty across a range of timescales by utilising important ocean temperature estimations as proxies for regional thermosteric sea level component. The aim of this research is to propose novel method in climate change with water level rise analysis using biosensor with machine learning techniques in diverse biological ecosystems. Here, the input is collected as biosensor-based climate analysis with water level rise analysis dataset and processed for noise removal, normalisation, and smoothening. Then, climate data analysis is carried out utilising fuzzy adversarial encoder (FAE) model and the water level rise analysis is carried out using recurrent transfer AlexNet neural network (RTAlexNetNN). The classified output shows climate change analysis with water level rise modelling. The experimental analysis has been carried out for various climate data and water rise data in terms of random accuracy, specificity, MSE, F-measure, and normalised cross-correlation. The proposed technique obtained 98% random accuracy, 96% normalised cross-correlation, 94% specificity, 58% MSE, and 92% F-measure.

Abstract

The frequency of extreme hydrologic events such as floods, storm surges, droughts, heat waves, extreme precipitation, and other similar occurrences has been increasing in Bangladesh due to the impact of climate change. Therefore, the assessment of changes in future climates is essential for climate-induced risk management in the country to safeguard natural resources and human lives. The main purpose of the current study is to assess the trend of maximum temperature (T_max), minimum temperature (T_min), and precipitation for the north-west region of Bangladesh in seasonal and annual scales for three future periods, including 2025–2050, 2051–2080, and 2081–2100, respectively. In order to achieve this goal, a large-scale atmospheric dataset obtained from the well-known general circulation model (GCM), CanESM2, is downscaled to finer scales at the local level using the widely used statistical downscaling model (SDSM). The downscaling of local climate variables is carried out using daily observed climate data under three representative concentration pathways (RCP) scenarios, including RCP2.6, RCP4.5, and RCP8.5, respectively. Correlation matrices with p-values have been utilized to select the most suitable predictors from NCEP/NCAR reanalysis data. Both the calibration (0.87 < R2 < 0.98, 0.87 < EV < 0.99, 19.24 > SE < 0.12) and validation findings demonstrate that the model performs satisfactorily. The bias correction approach is also adopted to achieve more consistent results. Seasonally, the mean seasonal temperature and precipitation are projected to rise in all seasons (except winter for precipitation). Annually, T_max and T_min have grown by 0.49 °C and 1.36 °C, respectively, whereas precipitation has increased by 49% up to the next century considering the RCP8.5 scenario (worst case). Overall, the outcome of the current study is expected to be supportive to policymakers and water managers in planning climate-resilient agricultural and infrastructure development activities for managing climate-induced disastrous events in the north-west region of Bangladesh.

Abstract

The study investigates the interplay of land use dynamics and climate change on the hydrological regime of the Ravi River using a comprehensive approach integrating Geographic Information System (GIS), remote sensing, and hydrological modeling at the catchment scale. Employing the Soil and Water Assessment Tool (SWAT) model, simulations were conducted to evaluate the hydrological response of the Ravi River to both current conditions and projected future scenarios of land use and climate change. This study differs from previous ones by simulating future land use and climate scenarios, offering a solid framework for understanding their impact on river flow dynamics. Model calibration and validation were performed for distinct periods (1999–2002 and 2003–2005), yielding satisfactory performance indicators (NSE, R², PBIAS = 0.85, 0.83, and 10.01 in calibration and 0.87, 0.89, and 7.2 in validation). Through supervised classification techniques on Landsat imagery and TerrSet modeling, current and future land use maps were generated, revealing a notable increase in built-up areas from 1990 to 2020 and projections indicating further expansion by 31.7% from 2020 to 2100. Climate change projections under different socioeconomic pathways (SSP2 and SSP5) were derived for precipitation and temperature, with statistical downscaling applied using the CMhyd model. Results suggest substantial increases in precipitation (10.9 − 14.9%) and temperature (12.2 − 15.9%) across the SSP scenarios by the end of the century. Two scenarios, considering future climate conditions with current and future land use patterns, were analyzed to understand their combined impact on hydrological responses. In both scenarios, inflows to the Ravi River are projected to rise significantly (19.4 − 28.4%) from 2016 to 2100, indicating a considerable alteration in seasonal flow patterns. Additionally, historical data indicate a concerning trend of annual groundwater depth decline (0.8 m/year) from 1996 to 2020, attributed to land use and climate changes. The findings underscore the urgency for planners and managers to incorporate climate and land cover considerations into their strategies, given the potential implications for water resource management and environmental sustainability.

Abstract

Under global warming, extreme events have been increasing in the last decade and are projected to increase in the future with every increment of global warming. The potential increase in compound drought and hot events may induce a complex web of impacts on societies, ecosystems, and economies, including crop failure, wildfires, and water scarcity. This is particularly concerning for Brazil, where it has been demonstrated to be vulnerable to recent extreme climate events. Using an ensemble of CORDEX-CORE simulations over Tropical Brazil, we investigate changes in compound events in response to changes in radiative forcing and their impact on climate extreme events, including drought and extreme heat. The simulations are conducted at a 25 km horizontal grid spacing using lateral and lower boundary forcing from three Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. Each model covers the period from 1980 to 2100 under two Radiative Concentration Pathways (RCP2.6 and RCP8.5) in the 21st-century projection period. We used observed data from the Brazilian Daily Weather Gridded Data (BR-DWGD) to evaluate the simulations and perform a quantitative assessment of areas affected by these compound events during the present day. The study finds a generally good agreement between RCM simulations and observed data, with moderate to high correlation coefficients for precipitation, though the strength of these correlations varies across different regions and seasons. The analysis emphasizes the prevalence of compound climate events during the Austral summer season and projects a significant increase in both extreme heat and drought events in the coming decades. These findings underscore Brazil’s vulnerability to compound climate events, highlighting the need for adaptive strategies and policy interventions to mitigate the socio-economic and environmental impacts across various sectors.

Abstract

Little is known about the effect of future weather and climate on municipal water demand in coastal communities with tourist-centric economies. To address this knowledge gap, we used an econometric model of monthly water demand that allowed for non-linear responses to weather variables to estimate temperature-response functions for demand from a sample of communities in the Oregon Mid-Coast. A main result is that local temperature was not a significant driver of variability in monthly water demand but that temperature in the Willamette Valley—the source of most tourists to the Oregon coast—was. We assumed that the increase in demand in response to higher Willamette Valley temperature arose from an increase in tourists escaping the heat in the Willamette Valley for cooler conditions on the coast. Applying the temperature response functions to scenarios of future climate to the year 2070 led to projected increases in water demand independent of other factors. Whether future tourism is either constrained by the local resident population that serves tourism or is constrained by the potential tourist population in the Willamette Valley, the climate-change contribution to projected water demand is generally of comparable magnitude to—if not greater than—the contribution from resident population change alone over the next 50 years. For communities where the population is projected to decline, the climate effect may more than offset the effect of declining population, resulting in a net positive change in demand.