Tag: Climate Change Downscaling Techniques

Abstract

Monitoring the changes in population and anthropogenic CO₂ emissions caused by geopolitical conflicts is significant for humanitarian assistance and also for revealing the CO₂ emission patterns of human activities. However, the changes in population and anthropogenic CO₂ emissions are highly dynamic, and representative survey data are generally unavailable. We monitor the near-real-time and fine-grained changes in war-induced population and anthropogenic CO₂ emissions in Ukraine through social media data. One year after the invasion, over 11 million Ukrainians are displaced from the baseline \({0.1}^{\circ}\,*\,{0.1}^{\circ}\) gridded regions. There is a significant correlation between the estimated changes and the reference changes for each month in all CO₂ emission sectors, with R² respectively ranging from 0.57 to 0.93, 0.41 to 0.9, 0.74 to 0.99 for residential consumption, ground transport, and industry sectors. Overall, the proposed method provides a new perspective to monitor Ukrainian refugee crisis and measure the spatio-temporal response of anthropogenic CO₂ emissions.

Abstract

Rainstorm is one of the global meteorological disasters that threaten the safety of transportation infrastructure and the connectivity of transportation system. Aiming to support the resilience assessment of transportation infrastructure in three representative regions: Sichuan–Chongqing, Yangtze River Delta, and Beijing-Tianjin-Hebei-Shandong, rainfall data over 40 years in the three regions are collected, and the temporal distribution of rainfall are analyzed. Prediction equations of rainfall are established. For the purpose of this, the probabilistic density function (PDF) is assigned to the rainfall by fitting the frequency distribution histogram. Using the assigned PDF, the rainfall data are transformed into standard normal space where regression of prediction equations is performed and the prediction accuracy is tested. The results show that: (1) The frequency of rainfall in the three regions follows a lognormal distribution based on which the prediction equations of rainfall can be established in standard normal space. The error of regression shows no remarkable dependence on self-variables, and the significance analysis indicates that the equations proposed in this paper are plausible for predicting rainfalls for the three regions. (2) The Yangtze River Delta region has a higher risk of rainstorm disaster compared to the other two regions according to the frequency of rainfall and the return period of precipitation concentration. (3) Over the period of 1980–2021, the Sichuan–Chongqing region witnessed an increase in yearly rainfall but a decrease in rainstorm disasters, whereas the other two regions experienced a consistent rise in both metrics.

Abstract

Flash floods stand as a substantial peril linked to climate change, imposing a severe menace to both human existence and built structures. This study aims to assess and compare the effectiveness of four distinct machine learning (ML) methodologies in the production of flood susceptibility maps (FSMs) in Ibaraki prefecture, Japan. Additionally, the investigation aims to examine the influence of excluding plan and profile curvature factors on the accuracy of the resulting maps. The dataset comprised 224 spots, consisting of 112 flooded and 112 non-flooded locations, and 11 environmental factors. The models were trained using 70% of the dataset, while the remaining 30% was utilized for model evaluation using the ROC curve method. The results indicated that both the ANN-MLP and SVR models achieved notable accuracy, with area under curve values of 95.23% and 95.83% respectively. An intriguing observation was made when the plan and profile curvature factors were excluded, as it led to an improvement in the accuracy of the ANN-MLP model, resulting in an accuracy of 96.7%. Furthermore, the generated FSMs were classified into five distinct hazard levels. The northern region of the maps predominantly exhibited very low and low hazard levels, while areas located in the southern region, closer to main streams, demonstrated considerably higher hazard levels categorized as very high and high. Ultimately, this study marks novel endeavor to investigate the impact of the curvature factor on the precision of machine learning algorithms in the creation of FSMs, which serve as fundamental tools for subsequent investigations.

Abstract

Future changes in streamflow and sediment, influenced by anthropogenic activities and climate change, have a crucial role in watershed management. This study aimed to quantify the effects of anthropogenic and natural drivers on future streamflow and sediment changes in the tropical Sai Gon Dong Nai River basin using the Soil and Water Assessment Tool (SWAT) model. Specifically, the model incorporated thirty-six reservoirs and analyzed twenty future climate projected scenarios from four Coupled Model Intercomparison Project Phase 6 (CMIP6) General Circulation Models (GCMs) for 2023–2100. These models include BCC-CSM2-MR (China), CanESM5 (Canada), MIROC6 (Japan), and MRI-ESM2-0 (Japan). Our findings indicate that (1) dam operation and diversion lead to a 0.5% decrease in streamflow during the dry season and a 4.1% increase during the rainy season compared to those in scenarios without dams; (2) there is a 37.4% decrease in annual sediment across the entire basin under same climate conditions; and (3) rainfall is projected to decrease (24.6% – 6.2%), resulting in a decrease in streamflow (0.2 – 32.2%) and sediment (39.3 – 56.0%) compared to historical records. Streamflow is expected to decrease during the rainy season (16.7 – 23.1%) and increase during the dry season (14.5 – 25.4%). Further potential degradation of the environmental conditions and water mismanagement are caused by the synergies between too much and too little rainfall conditions. The anticipated reductions in future streamflow and sediment could adversely affect ecological streamflow, water security, and sediment dynamics in the Sai Gon Dong Nai River basin. Our approach effectively identifies future changes in streamflow and sediment due to the combined effects of climate change and reservoir operations, providing valuable insights for integrated water resource management in tropical regions.

Abstract

This study investigates the impact of enhanced horizontal resolution on simulating mean and extreme precipitation in the major river basins of southern Africa. Seven global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) High-Resolution Model Intercomparison Project (HighResMIP) are used, which are available at both high-resolution (HR) and low-resolution (LR). The models are assessed using three observational datasets from 1983–2014 during December-January–February. The performance of the models in simulating nine extreme precipitation indices, as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), is quantitatively assessed using various of statistical metrics. Results show that the distributions of daily precipitation from the HR models are nearly identical to those of their LR counterparts. However, model biases are not consistent across the three observations. Most HR and LR models reasonably simulate mean precipitation, maximum consecutive dry and wet days (CDD and CWD), number of rainy days (RR1) and heavy precipitation events (R10mm and R20mm), albeit with some biases. Enhanced horizontal resolution improves the simulation of mean precipitation, CDD, CWD, RR1 and R10mm, as indicated by high spatial correlation coefficients (SCCs), low root mean square errors (RMSEs), and reduced biases in most HR models. The majority of the HighResMIP models (i.e., both LR and HR models) overestimates extreme wet days precipitation (R95p and R99p), maximum one-day precipitation (Rx1day), and simple daily intensity (SDII), with a pronounced wet bias in HR models for R95p and R99p. Most LR models outperforms HR models in simulating R95p, R99p, and SDII. By means of a Comprehensive Ranking Metrics, EC-EARTH_HR is identified as the best performing model for simulating all nine extreme precipitation indices across the basins, except for the Zambezi, where EC-EARTH_LR performs best. Our findings indicate that increased resolution can either improve or worsen performance depending on the model and basin. Therefore, no clear evidence exist that enhanced horizontal resolution under HighResMIP enhances the simulation of extreme precipitation in southern Africa.

Abstract

A statistical downscaling of wind and wave regimes is presented. The study is around the Cuban archipelago for the mid-term (2031–2060) and the long-term (2061–2090) with respect to the historical period 1976–2005. A multimodel ensemble of CMIP5 models under the RCP4.5 and the RCP8.5 scenarios is used. Projections of the wind and wave regimes are projected through the BIAS correction (delta and empirical quantile mapping), and multiple regression with a determination coefficient of 88.3%, a residual standard deviation of 0.11, and a square mean error of 0.29. According to the statistical downscaling, the mean annual wind speed and the wave height showed significant changes in the western part of the Cuban archipelago. The extreme indicators of climate change referred to significant wave height show similarity in the representation of the future Cuban marine climate, which would have the most accentuated changes on the north coast of the central and eastern regions.

Abstract

Water availability is of paramount importance for sustainable development and environmental planning, specifically in regions such as the Iberian Peninsula, renowned for diverse landscapes and varying climatic conditions. Due to climate change, understanding the potential impacts on water resources becomes essential for effective water management strategies. This research effort aims to assess future potential water availability for the Iberian Peninsula in different climate scenarios, employing cutting-edge water resource modelling techniques integrated within a geographic information system (GIS) framework. In this study, potential water availability is defined as the annual demand for water that can be satisfied at a specific point in the fluvial network with certain reliability. An ensemble of state-of-the-art climate models is utilised to project runoff for the Iberian Peninsula during the mid- and late-twenty-first century periods. These climate projections were subsequently processed using the GIS-based water resource management model, WAAPA, to derive potential water availability under a range of realistic hypotheses. The results indicate that anticipated shifts in precipitation patterns will lead to alterations in hydrological regimes across the region, significantly impacting future water availability. By using GIS-based methodologies, we can facilitate the identification of vulnerable areas susceptible to changes in water availability, offering spatially explicit information along the main rivers of the Iberian Peninsula for decision-makers and stakeholders. High-resolution spatial outputs from this research and detailed water availability estimates serve as valuable input for integrated water resource management and climate change adaptation planning. By combining advanced GIS-based hydrological modelling with climate scenarios, this research presents a robust framework for assessing water resources amidst a changing climate, applicable to other regions struggling with analogous challenges. Ultimately, our study provides vital insights for policymakers and stakeholders, empowering them to make informed decisions and devise adaptive measures to ensure sustainable use of water resources despite uncertain future climatic conditions.

Abstract

Small Caribbean islands are on the frontline of climate change because of sea level rise, extreme rainfall and temperature events, and heavy hurricanes. The Archipelago of San Andrés, Providencia, and Santa Catalina (SAI), are Caribbean islands belonging to Colombia and declared a Biosphere Reserve by UNESCO. SAI is highly vulnerable to climate change impacts but no hydroclimatological study quantified the extreme climatic changes yet. This study analyzes historical (1960s-2020, 7 stations) and future (2071–2100, CMIP6 multi-model ensemble, for four scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) trends in mean and extreme precipitation and temperature duration, frequency, and intensity. We find that heatwaves have more than tripled in frequency and doubled their maximum duration since the end of the ‘80 s. Precipitation is historically reduced by 5%, with a reduction recorded in 5 stations and an increase in 2, while extreme rainfall events significantly increased in frequency and intensity in most stations. The hotter-and-drier climate is amplified in the future for all scenarios, with much drier extremes (e.g., -0.5─-17% wet days, +8%─30% consecutive dry days, and +60%─89% in hot days). Although we show that hurricanes Categories IV and V near SAI (< 600 km) more than doubled since the’60 s, only a small fraction of extreme rainfall in the archipelago is associated with hurricanes or tropical storms. La Niña events also have no substantial influence on extreme precipitation. Interestingly, opposite and heterogeneous historical extreme rainfall trends are found across such small territory (< 30 km²). Thus, downscaled hydrometeorological data and model simulations are essential to investigate future extreme climatic events and strengthen small Caribbean islands' climate change adaptation efforts.

Abstract

The Taihangshan-Yanshan region (TYR) is an important ecological barrier area for Beijing-Tianjin-Hebei, and the effectiveness of its ecological restoration and protection is of great significance to the ecological security pattern of North China. Based on the FVC data from 2000 to 2021, residual analysis, parametric optimal geodetector technique (OPGD) and multi-scale geographically weighted regression analysis (MGWR) were used to clarify the the multivariate driving mechanism of the evolution of FVC in the TYR. Results show that: (1) FVC changes in the TYR show a slowly fluctuating upward trend, with an average growth rate of 0.02/10a, and a spatial pattern of "high in the northwest and low in the southeast"; more than half of the FVC increased during the 22-year period. (2) The results of residual analysis showed that the effects of temperature and precipitation on FVC were very limited, and a considerable proportion (80.80% and 76.78%) of the improved and degraded areas were influenced by other factors. (3) The results of OPGD showed that the main influencing factors of the spatial differentiation of FVC included evapotranspiration, surface temperature, land use type, nighttime light intensity, soil type, and vegetation type (q > 0.2); The explanatory rates of the two-factor interactions were greater than those of the single factor, which showed either nonlinear enhancement or bifactorial enhancement, among which, the interaction of evapotranspiration with mean air and surface temperature has the strongest effect on the spatial and temporal evolution of FVC (q = 0.75). Surface temperature between 4.98 and 10.4 °C, evapotranspiration between 638 and 762 mm/a, and nighttime light between 1.96 and 7.78 lm/m² favoured an increase in vegetation cover, and vegetation developed on lysimetric soils was more inclined to be of high cover. (4) The correlation between each variable and FVC showed different performance, GDP, elevation, slope and FVC showed significant positive correlation in most regions, while population size, urban population proportion, GDP proportion of primary and secondary industries, and nighttime light intensity all showed negative correlation with FVC to different degrees. The results can provide data for formulating regional environmental protection and restoration policies.

Abstract

The impacts of tropical cyclones (TCs) on Southeast Asia’s coastlines are acute due to high population densities in low-lying coastal environments. However, the trajectories of TCs are uncertain in a warming climate. Here, we assess >64,000 simulated TCs from the nineteenth century to the end of the twenty-first century for both moderate- and high-emissions scenarios. Results suggest changes to TC trajectories in Southeast Asia, including: (1) poleward shifts in both genesis and peak intensification rates; (2) TC formation and fastest intensification closer to many coastlines; (3) increased likelihoods of TCs moving most slowly over mainland Southeast Asia; and (4) TC tracks persisting longer over land. In the cities of Hai Phong (Vietnam), Yangon (Myanmar), and Bangkok (Thailand), these variations result in future increases in both peak TC intensity and TC duration compared to historical TCs.