Month: August 2024
Projected changes in precipitation extremes in Southern Thailand using CMIP6 models
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.
Assessments of various precipitation product performances and disaster monitoring utilities over the Tibetan Plateau
Abstract
The Tibetan Plateau, often referred to as Asia’s water tower, is a focal point for studying spatiotemporal changes in water resources amidst global warming. Precipitation is a crucial water resource for the Tibetan Plateau. Precipitation information holds significant importance in supporting research on the Tibetan Plateau. In this study, we estimate the performance and applicability of Climate Prediction Center Merged Analysis of Precipitation (CMAP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Land Data Assimilation System (GLDAS), and Global Precipitation Climatology Project (GPCP) precipitation products for estimating precipitation and different disaster scenarios (including extreme precipitation, drought, and snow) across the Tibetan Plateau. Extreme precipitation and drought indexes are employed to describe extreme precipitation and drought conditions. We evaluated the performance of various precipitation products using daily precipitation time series from 2000 to 2014. Statistical metrics were used to estimate and compare the performances of different precipitation products. The results indicate that (1) Both CMAP and IMERG showed higher fitting degrees with gauge precipitation observations in daily precipitation. Probability of detection, False Alarm Ratio, and Critical Success Index values of CMAP and IMERG were approximately 0.42 to 0.72, 0.38 to 0.56, and 0.30 to 0.42, respectively. Different precipitation products presented higher daily average precipitation amount and frequency in southeastern Tibetan Plateau. (2) CMAP and GPCP precipitation products showed relatively great and poor performance, respectively, in predicting daily and monthly precipitation on the plateau. False alarms might have a notable impact on the accuracy of precipitation products. (3) Extreme precipitation amount could be better predicted by precipitation products. Extreme precipitation day could be badly predicted by precipitation products. Different precipitation products showed that the bias of drought estimation increased as the time scale increased. (4) GLDAS series products might have relatively better performance in simulating (main range of RMSE: 2.0–4.5) snowfall than rainfall and sleet in plateau. G-Noah demonstrated slightly better performance in simulating snowfall (main range of RMSE: 1.0–2.1) than rainfall (main range of RMSE: 2.0–3.8) and sleet (main range of RMSE: 1.5–3.8). This study’s findings contribute to understanding the performance variations among different precipitation products and identifying potential factors contributing to biases within these products. Additionally, the study sheds light on disaster characteristics and warning systems specific to the Tibetan Plateau.
The impact of climate change on Al-wala basin based on geomatics, hydrology and climate models
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°- 0.11o 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.
Impact of climate change on the behaviour of solar radiation using AFR-CORDEX model over West Africa
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.
Modeling the impact of climate change on wheat yield in Morocco based on stacked ensemble learning
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.
Comprehensive spatial analysis landslide susceptibility modelling, spatial cluster analysis and priority zoning for environment analysis
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.
Citizens’ deprecating behaviour: dragging down the nation branding efforts in developing countries—opinions of selected stakeholders in Zimbabwe
Abstract
Developing countries face unique challenges in building and managing their national image due to various socio-political and economic factors. Thus, this study explored the extent of citizens’ deprecating behaviour and factors that breed citizens’ deprecating behaviour focusing on Zimbabwe as a case study. An interpretivist philosophy based on the exploratory approach was employed where a total of 20 personal interviews were conducted with purposively selected government officials, business leaders/marketing experts, sports personalities, media experts, local university students, and international diplomats. Findings indicated a high prevalence of citizens’ deprecating behaviour while national injustices, economic mismanagement, bad governance, foreign government interference, and human rights abuse top the list of factors breeding citizens’ deprecating behaviour. The study recommends the government and the nation branding stakeholders to adopt an inclusive approach in implementing strategies that result in the effective development of a compelling nation brand while promoting social cohesion and equity among the citizens.
Pushing the frontiers in climate modelling and analysis with machine learning
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.
Fingerprints of past volcanic eruptions can be detected in historical climate records using machine learning
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.