Tag: Climate Change Downscaling Techniques

Abstract

Southern Thailand has experienced significant shifts in precipitation patterns in recent years, exerting substantial impacts on regional water resources and infrastructure systems. This study aims to elucidate these changes and underlying factors based on daily precipitation observations from Nakhon Si Thammarat Province spanning 1980 to 2022. Additionally, data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) is utilized to investigate projected changes in precipitation for 2015–2100 relative to the historical period (1980–2014), employing a comprehensive analysis considering two emissions scenarios (SSP245 and SSP585) across six models. Various precipitation indices are selected to assess trends and statistical significance using the Mann-Kendall test. Both observed and climate model data indicate an increasing precipitation trend in Southern Thailand, with a reduced association with the El Niño-Southern Oscillation (ENSO) under warming conditions. Extreme precipitation indices also exhibit an increasing trend, with total precipitation and the 95th percentile of daily precipitation (R95p) revealing very wet conditions in recent years, projected to continue increasing. Contrastingly, the number of dry days is also mounting, suggesting that both dry and wet extremes will impact Southern Thailand under a warmer climate. The findings from this study provide an early indication of future precipitation and extreme event scenarios, which can inform the development of measures to mitigate climate change-related hazards in the region.

Abstract

Jordan is severely affected by climate change, it suffers from significance fluctuation and decrease in the amounts of the annual precipitation basically during the last decade which had dire consequences for farmers and the provision of fresh water. In this study, the impact of climate change on the Al-Wala basin was analyzed during the period 2013 to 2024 using Geomatics techniques, Google Earth Engine (GEE) and machine learning codes. Soil and Water Assessment Tool (SWAT) model was used to simulate the hydrological process up to year 2064. Moreover, the Meteorological Research Institute Earth System Model (MRI-ESM2-0) was used to predict the change of water surface area of the Al-Wala dam lake in the future. Annual satellite images: Lanadsat and sentinel, covering the period of the study area were downloaded and enhanced. They permit to provide the necessary information to carry out this study. As result, an important fluctuation of the amount of annual rainfall quantity was observed as well as, the amounts of annual rainfall expected to increase and decrease wobbly for several years in the future. Overall the average annual runoff will increase by 10% compared to the baseline scenario. The minimum temperature is expected to be higher than their rates throughout the year by 0.09°- 0.11^o C, this will increase the evaporation rates with about 0.03%. The analysis of the sensitivity using the SWAT model was identified by 6 parameters out of 17. The regression coefficient (R²), Nash and Sutcliffe efficiency (NSE), on monthly basis, were above 0.60 for both of them which indicates satisfactory model results.

Abstract

This research discusses the application of comprehensive analysis in the study of landslide susceptibility in the Sumberwangi watershed and the Dilem-Wilis area, specifically on the slopes of the Liman-Wilis Mountains, East Java Province, Indonesia. Landslide characterization and inventory, the Weight of Evidence Model used in identifying landslide susceptibility zones (LSZ), landslide susceptibility hot spot analysis, and priority zoning mapping were implemented in this research. The results show that the distribution of landslides increases as the slope slope increases. The genetic presence of the B horizon causes shallow landslides in the study area. LSZ spatial modelling demonstrates good accuracy for conducting applied analysis, with area ratio values of 0.859 and precision-recall of 0.829. Information Value is used to determine the factors that have the model's predictive capability. Elevation, road density, terrain ruggedness index, slope, stream power index, stream density, and landform factors are strong predictive capabilities. This research introduces hot spot analysis from the LSZ model, especially the Dilem Wilis area, to identify statistically significant landslide-prone areas. Subsequently, a priority zoning map was created based on Frequency Ratio analysis, comparing the number of landslide-prone hot spots with the area size of each subgroup based on the agro-tourism and STP zoning plan maps. A comprehensive analysis of priority zoning maps and landslide impacts based on an environmental geography perspective is presented in this research.

Abstract

Climate change increases the frequency and intensity of extreme events such as droughts, heat waves, and floods, posing a significant challenge to Morocco’s agriculture and food security. Understanding the future impact of climate on crop yield is crucial for long-term agricultural planning. However, this area has been underexplored due to various challenges, including data constraints. This study aimed to project wheat yield in Morocco at a provincial scale from 2021 to 2040 by using multiple climate model datasets, and advanced Machine Learning (ML) algorithms. An ensemble of five global climate models (MIROC6, CanESM5, IPSL-CM6A-LR, INM-CM5-0, NESM3) was employed to project changes in temperature (Tmax, Tmin) and precipitation (Pr). The climate projections were bias corrected using quantile-quantile approach. Four advanced ML algorithms: Random Forest, XGBoost, LightGBM, and Gradient Boosting Regressor, were utilized to develop a stacked ensemble learning model for wheat yield prediction at provincial scale in Morocco. The stacked ensemble learning model was calibrated and validated using historical wheat yield data. Results show that the stacked ensemble learning approach significantly reduced prediction errors compared to individual models, achieving high coefficient of determination of 0.82 and low root mean square error (RMSE) of 300.51 kg/ha. Wheat yields are projected to decline by an average of 10% by 2040 under the modest shared socioeconomic pathways (SSP2-4.5) scenario while under high emission scenario (SSP5-8.5), yield could decrease by up to 60% across some provinces such as Essaouira, Youssoufia, Ouezzane, Rehamna, and Sidi Kacem. Temperature (Tmax and Tmin) and precipitation (Pr) were identified as the critical climate variables influencing wheat yield, with Tmax being the most impactful. Regional projections revealed that provinces inland and in southern Morocco may experience a significant yield reduction of up to 60%. This study highlights the need for implementing effective climate change mitigation measures to avert food insecurity in Morocco and other northern African countries. The primary findings indicate that climate variables, particularly Tmax, play a crucial role in wheat yield projections, emphasizing the importance of detailed climate data and advanced modeling techniques in agricultural planning.

Abstract

The study evaluated the impact of climate change on incoming solar radiation (RSDS) in West Africa by comparing observed data from the CMSAF solar products (SARAH and CLARA-A1) for the period 1983–2019 with simulated data from the AFR-CORDEX models (RegCM-4.7 and CCCma-canRCM4) for the historical period (1983–2004) and various RCP emission scenarios (2.6, 4.5, 8.5) for 2005–2099. The values of the RCP in parentheses signify the level of increasing radiative forcings due to varying emission controls. Assessment metrics like correlation coefficient (R), Taylor Skill Score (TSS), and root mean square errors (RMSE) were employed for comparative analysis on annual and seasonal timescales. The analyses revealed annual mean RSDS intensities of 256.22 for SARAH, 238.53 for CLARA-A1, 270.81 for Historical, 270.26 for RCP 2.6, 255.90 for RCP 4.5, and 271.93 for the RCP 8.5 scenarios in watts per square metres. The TSS analyses showed average agreement values between observed CMSAF and simulated AFR-CORDEX solar radiation with values of 0.8450 and 0.8575 with historical, 0.8750 and 0.8600 with RCP 2.6, 0.9025 and 0.8550 with RCP 4.5, and 0.8675 and 0.8525 with RCP 8.5 scenarios for SARAH and CLARA-A1 respectively. All the metrics showed better agreement with SARAH than CLARA-A1, likely due to the associated cloud influence on CLARA-A1. Notably, the CORDEX-CCCma-canRCM4 model under RCP 4.5 demonstrated the highest accuracy, with an average correlation of 0.82 and a mean TSS of 0.90 against the SARAH reference dataset. The results suggest the AFR-CORDEX model, particularly the CCCma-canRCM4 for RCP 4.5 scenario, could reliably predict solar radiation and inform climate change impacts on solar energy potential in West Africa under moderate emission conditions.

Abstract

Accurately interpreting past climate variability, especially distinguishing between forced and unforced changes, is challenging. Proxy data confirm the occurrence of large volcanic eruptions, but linking temperature patterns to specific events or origins is elusive. We present a method combining historical climate records with a machine learning model trained on climate simulations of various volcanic magnitudes and locations. This approach identifies volcanic events based solely on post-eruption temperature patterns. Validations with historical simulations and reanalysis products confirm the identification of significant volcanic events. Explainable artificial intelligence methods point to specific fingerprints in the temperature record that reveal key regions for classification and point to possible physical mechanisms behind climate disruption for major events. We detect unexpected climatic effects from smaller events and identify a northern extratropical footprint for the unidentified 1809 event. This provides an additional line of evidence for past volcanoes and refines our understanding of volcanic impacts on climate.

Abstract

Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science.

Abstract

Climate change has significantly impacted the1 water resources and conservation area of the Yellow River basin. The Upper Yellow River basin (UYR), referring to the area above Lanzhou station on the Yellow River is the focus of this study, the runoff changes in the UYR would greatly impact the water resources in China. Most existing studies rely on a single hydrological model (HM) to evaluate runoff changes instead of multiple models and criteria. In terms of the UYR, outputs of the previous Coupled Model International Comparison Project (CMIP) are used as drivers of HMs. In this study, the weighted results of three HMs were evaluated using multiple criteria to investigate the projected changes in discharge in the UYR using the Shared Socioeconomic Pathways (SSPs) from CMIP6. The research’s key findings include the following. 1) Annual discharge in the UYR is expected to increase by 15.2%–64.4% at the end of the 21st century under the 7 SSPs. In the long-term (2081–2100), the summer and autumn discharge will increase by 18.9%–56.6% and 11.8%–70%, respectively. 2) The risk of flooding in the UYR is likely to increase in the three future periods (2021–2040, 2041–2060, 2081–2100) under all 7 SSPs. Furthermore, the drought risk will decrease under most scenarios in all three future periods. The verified HMs and the latest SSPs are applied in this study to provide basin-scale climate impact projections for the UYR to support water resource management.

Abstract

Climate internal variability plays a crucial role in the hydroclimate system, and this study quantifies its predictability on streamflow in California using historical observations, climate simulations, and various machine learning (ML) models. Here we demonstrate that while 5% of the year-to-year variability in seasonal peak streamflow can be attributed to the well-known climate variability indices, the explained variance surpasses 30% when higher-order empirical orthogonal functions of these indices are retained in the analysis. Notably, the results highlight the significant influence of the 5^th empirical mode of the Pacific North American pattern and of the Pacific Decadal Oscillation in shaping the streamflow variability, which is consistent across all the tested ML models. A deeper investigation reveals a clear and monotonic quasi-linear response of streamflow to these dominant patterns, emphasizing the substantial role played by higher-order internal modes of variability in shaping regional hydroclimate systems, which contributes to bridging the gap between the well-known variability domains and local climate systems.

Abstract

Addressing the urgency of climate change necessitates a coordinated and inclusive effort from all relevant stakeholders. Critical to this effort is the modelling, analysis, control and integration of technological innovations within the electric energy system, which plays a major role in scaling up climate change solutions. This Perspective presents a set of research challenges and opportunities in the area of electric power systems that would be crucial in accelerating gigaton-level decarbonization. Furthermore, it highlights institutional challenges associated with developing market mechanisms and regulatory architectures, ensuring that incentives are aligned for stakeholders to effectively implement the technological solutions on a large scale.