Tag: Climate Change Downscaling Techniques

Abstract

Bangladesh, a sub-tropical monsoon climate with low-lying areas, is very susceptible to the impacts of climate change. However, there has been a shortage of studies about the periodicity and projected changes in extreme temperature in this area, which is a crucial part of adapting to climate change. A study employed a multimodal ensemble (MME) mean of 13 bias-corrected CMIP6 GCMs to fill this knowledge gap. The purpose of this study was to project changes in 8 extreme temperature indices (ETIs) across Bangladesh for the near future (2021–2060) and far future (2061–2100) under two different Shared Socioeconomic Pathways (SSPs): medium (SSP2-4.5) and high (SSP5-8.5) scenarios. The research analyzed the average spatiotemporal changes by considering the reference period from 1995 to 2014 for each indicator in future periods. The results indicate that Bangladesh is projected to see a rise in average annual temperature in the 21st century, aligning with the global average. Warm days (TX90p) and nights (TN90p) were projected to increase, while cold days (TX10p) and nights (TN10p) were expected to decrease across the country for both the near (2021–2060) and far future (2061–2100). The projected highest increase in TX90p and TN90p was 6.90 days/decade in the northeast, and the highest decrease in TX10p and TN10p was 6.22 days/decade in the southwest. The study revealed a higher rise in TN90p than TX90p, indicating a faster decline in cold extremes than a rise in hot extremes. The rising temperature would cause an increase in the spell duration index (WSDI) and growing degree day (GDD) by 5–6 and 6–7 days/decade, respectively. Therefore, immediate measures must be taken to mitigate the detrimental effects of extreme temperatures, leading to heat stress. To reduce the effects on agriculture, ecosystems, human health, and biodiversity, policymakers and stakeholders must understand these anticipated changes and adopt appropriate actions.

Abstract

The widely distributed sediments following an earthquake presents a continuous threat to local residential areas and infrastructure. These materials become more easily mobilized due to reduced rainfall thresholds. Before establishing an effective management plan for debris flow hazards, it is crucial to determine the potential reach of these sediments. In this study, a deep learning-based method—Dual Attention Network (DAN)—was developed to predict the runout distance of potential debris flows after the 2022 Luding Earthquake, taking into account the topography and precipitation conditions. Given that the availability of reliable precipitation data remains a challenge, attributable to the scarcity of rain gauge stations and the relatively coarse resolution of satellite-based observations, our approach involved three key steps. First, we employed the DAN model to refine the Global Precipitation Measurement (GPM) data, enhancing its spatial and temporal resolution. This refinement was achieved by leveraging the correlation between precipitation and regional environment factors (REVs) at a seasonal scale. Second, the downscaled GPM underwent calibration using observations from rain gauge stations. Third, mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were employed to evaluate the performance of both the downscaling and calibration processes. Then the calibrated precipitation, catchment area, channel length, average channel gradient, and sediment volume were selected to develop a prediction model based on debris flows following the Wenchuan Earthquake. This model was applied to estimate the runout distance of potential debris flows after the Luding Earthquake. The results show that: (1) The calibrated GPM achieves an average MAE of 1.56 mm, surpassing the MAEs of original GPM (4.25 mm) and downscaled GPM (3.83 mm); (2) The developed prediction model reduces the prediction error by 40 m in comparison to an empirical equation; (3) The potential runout distance of debris flows after the Luding Earthquake reaches 0.77 km when intraday rainfall is 100 mm, while the minimum distance value is only 0.06 km. Overall, the developed model offers a scientific support for decision makers in taking reasonable measurements for loss reduction caused by post-seismic debris flows.

Abstract

Post-pandemic green recovery is pivotal in achieving global sustainable development goals by simultaneously revitalizing economies and reducing greenhouse gas emissions, air pollution and improving public welfare. However, subnational and city-level understanding of green recovery, its efficacy and its alignment with public health is poorly understood. Here we focus on post-COVID-19 low-carbon recovery—economic growth combined with reduced carbon emissions—and explore health co-benefits in Chinese cities. A novel near-real-time daily carbon emission dataset of 48 cities in China is developed, coupled with detailed health and economic municipal statistics and models. We find that, on average, six low-carbon-recovery cities, mainly megacities, saved 1.2 times as many lives per 100,000 population compared with the 42 other cities, and their annual monetary avoided premature deaths per 100,000 population was 1.5 times more than the 42 other cities. The accumulated monetary health co-benefits for low-carbon-recovery cities were US$ 4.2 billion (95% confidence interval, 2.1–6.3) during the post-COVID-19 period. We show that government spending on electric vehicles increases the likelihood of achieving low-carbon recovery in Chinese cities. Our results underscore the significant health co-benefits of low-carbon recovery, pointing to synergies between advancing local welfare and global environmental objectives.

Abstract

Numerous hydrological applications, such as soil erosion estimation, water resource management, and rain driven damage assessment, demand accurate and reliable rainfall erosivity data. However, the scarcity of gauge rainfall records and the inherent uncertainty in satellite and reanalysis-based rainfall datasets limit rainfall erosivity assessment globally. Here, we present a new global rainfall erosivity dataset (0.1° × 0.1° spatial resolution) integrating satellite (CMORPH and IMERG) and reanalysis (ERA5-Land) derived rainfall erosivity estimates with gauge rainfall erosivity observations collected from approximately 6,200 locations across the globe. We used a machine learning-based Gaussian Process Regression (GPR) model to assimilate multi-source rainfall erosivity estimates alongside geoclimatic covariates to prepare a unified high-resolution mean annual rainfall erosivity product. It has been shown that the proposed rainfall erosivity product performs well during cross-validation with gauge records and inter-comparison with the existing global rainfall erosivity datasets. Furthermore, this dataset offers a new global rainfall erosivity perspective, addressing the limitations of existing datasets and facilitating large-scale hydrological modelling and soil erosion assessments.

Abstract

Global Climate Models (GCMs) are very essential and crucial for projecting future climate scenarios under different greenhouse gas emissions, incorporating uncertainties in the global warming projections. The present study evaluates the seasonal performance of 32 Coupled Model Intercomparison Project Phase 5 (CMIP5) models obtained from the Coordinated Ocean Wave Climate Project phase 2 (COWCLIP 2.0) in simulating the global and regional wave power (WP) from 1979 to 2004 using historical data, and comparing them against the ERA5 reanalysis. Three skill metrics, such as Root Mean Square Error (RMSE), Interannual Variability Skill (IVS), and M-Score were used to assess the model performance across three clusters (CSIRO, JRC, and IHC). In addition, Intra-seasonal and probability distribution is also employed to determine the cluster’s performance, including individual models. The IHC cluster, employing statistical techniques, exhibited the lowest RMSE and highest M-Score values with the least variation among models over the global as well as regional ocean basins such as the North Atlantic (NA), North Pacific (NP), Indian Ocean (IO), and Pacific Ocean (PO. Results from intra-seasonal variability and probability distribution indicate that the IHC cluster demonstrates the most stable performance in simulating intra-seasonal variability of WP as compared to other clusters.

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.

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.