Tag: Climate Change Downscaling Techniques

Abstract

Due to the length and complexity of supply chains, changes in food consumption patterns in one metropolitan region can transform production patterns in other sectors and countries. Therefore, they cause complex synergies and trade-offs between environmental and socioeconomic goals at the local and global level. We argue that the dissemination of cross-scale consumption-based simulation models is crucial to investigate these complex multilevel effects and promote sustainable food systems.

Abstract

Leaf Area Index (LAI) is a critical parameter in terrestrial ecosystems, with high spatial resolution data being extensively utilized in various research studies. However, LAI data under future scenarios are typically only available at 1° or coarser spatial resolutions. In this study, we generated a dataset of 0.05° LAI (F0.05D-LAI) from 1983–2100 in a high spatial resolution using the LAI Downscaling Network (LAIDN) model driven by inputs including air temperature, relative humidity, precipitation, and topography data. The dataset spans the historical period (1983–2014) and future scenarios (2015–2100, including SSP-126, SSP-245, SSP-370, and SSP-585) with a monthly interval. It achieves high accuracy (R² = 0.887, RMSE = 0.340) and captures fine spatial details across various climate zones and terrain types, indicating a slightly greening trend under future scenarios. F0.05D-LAI is the first high-resolution LAI dataset and reveals the potential vegetation variation under future scenarios in China, which benefits vegetation studies and model development in earth and environmental sciences across present and future periods.

Abstract

Comprehensively analyzing carbon emissions in the Central Plains Urban Agglomeration (CPUA) of China is an effective case study for promoting sustainable development and supporting China in achieving its carbon peak targets. This study applies an energy balance sheet downscaling method to estimate the carbon emissions of 30 cities in the CPUA from 2000 to 2021, examining trends in carbon emissions and land carbon sequestration. Key influencing factors of carbon emissions are identified using knowledge graph technology, and the spatiotemporal effects of these factors are analyzed using Geographically and Temporally Weighted Regression Geographically Weighted Regression and Multiscale Geographically Weighted Regression models. The study shows that carbon emissions in the CPUA increased from 452.639 million tons in 2000 to 1737.107 million tons in 2021, with a growth rate that declined from 24.18% to 3.06%. Fossil fuel consumption and cultivated land were major carbon sources, while forest land was a significant carbon sink. The spatial pattern of carbon emissions predominantly showed lower values in the south and higher values in the north, with significant clustering in high emission areas. Population size, per capita gross domestic product, technological progress, and energy consumption intensity had significant impacts on the urban agglomeration’s carbon emissions. However, the impact was influenced by fluctuations driven by government policies, industrial and energy structures, and other factors. This study not only provides critical insights for China’s low-carbon development but also offers valuable lessons for other developing countries facing similar challenges. Urban agglomeration planning should focus on optimizing energy and industrial structures, promoting green technology, and designing tailored carbon reduction policies to achieve sustainable and green development.

Abstract

As the world’s largest energy consumer and carbon emitter, the task of carbon emission reduction is imminent. In order to realize the dual-carbon goal at an early date, it is necessary to study the key factors affecting China’s carbon emissions and their non-linear relationships. This paper compares the performance of six machine learning algorithms to that of traditional econometric models in predicting carbon emissions in China from 2011 to 2020 using panel data from 254 cities in China. Specifically, it analyzes the comparative importance of domestic economic, external economic, and policy uncertainty factors as well as the nonparametric relationship between these factors and carbon emissions based on the Extra-trees model. Results show that energy consumption (ENC) remains the root cause of increased carbon emissions among domestic economic factors, although government intervention (GOV) and digital finance (DIG) can significantly reduce it. Next, among the external economic and policy uncertainty factors, foreign direct investment (FDI) and economic policy uncertainty (EPU) are important factors influencing carbon emissions, and the partial dependence plots (PDPs) confirm the pollution haven hypothesis and also reveal the role of EPU in reducing carbon emissions. The heterogeneity of factors affecting carbon emissions is also analyzed under different city sizes, and it is found that ENC is a common driving factor in cities of different sizes, but there are some differences. Finally, appropriate policy recommendations are proposed by us to help China move rapidly towards a green and sustainable development path.

This report summarises the main outcomes of the 4th edition of the workshop on Machine Learning (ML) for Earth System Observation and Prediction (ESOP / ML4ESOP) co-organised by the European Space Agency (ESA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The 4-day workshop was held on 7-10 May 2024 in a hybrid format at the ESA Frascati site with an interactive online component, featuring over 46 expert talks with a record number of submissions and about 800 registrations. The workshop offered leading experts a platform to exchange on the current opportunities, challenges and future directions for applying ML methodology to ESOP. To structure the presentations and discussions, the workshop featured five main thematic areas covering key topics and emerging trends. The most promising research directions and significant outcomes were identified by each thematic area’s Working Group and are the focus of this document.

Abstract

There are millions of river barriers worldwide, ranging from wooden locks to concrete dams, many of which form associated impoundments to store water in small ponds or large reservoirs. Besides their benefits, there is growing recognition of important environmental and social trade-offs related to these artificial structures. However, global datasets describing their characteristics and geographical distribution are often biased towards particular regions or specific applications, such as hydropower dams affecting fish migration, and are thus not globally consistent. Here, we present a new river barrier and reservoir database developed by the Global Dam Watch (GDW) consortium that integrates, harmonizes, and augments existing global datasets to support large-scale analyses. Data curation involved extensive quality control processes to create a single, globally consistent data repository of instream barriers and reservoirs that are co-registered to a digital river network. Version 1.0 of the GDW database contains 41,145 barrier locations and 35,295 associated reservoir polygons representing a cumulative storage capacity of 7,420 km³ and an artificial terrestrial surface water area of 304,600 km².

Abstract

Hydrological models are used to assess natural and man-made changes in watersheds worldwide. Proper input data collection and handling are essential to reduce simulation uncertainty. Thus, this study reviews the sources of physical and hydroclimatic data, used in the last 5 years, from 55 articles that applied the SWAT model in Brazil. Most studies took place in the Atlantic Forest biome (20), followed by Cerrado (14), Amazon (11), and Caatinga (10). Worth noting that there are no studies published in the Pantanal and Pampa biomes. National databases (INPE, INMET, EMPRAPA, and ANA) are the most used in data acquisition process, followed by regional databases, more applied in smaller basins. Global databases are more sought after in studies of large basins due to their low spatial resolution. National climate data have low spatial density and are only available in five states at the regional level, so satellite data and reanalysis are viable alternatives in regions with little climate monitoring. Future research directions include (1) evaluating and comparing available data, (2) using high-resolution imagery to map land use in small catchments, (3) expanding the model’s database of vegetation parameters to cover all classes identified in high-resolution images, (4) create a database at regional level in the states, (5) develop software to manage hydroclimatic information, and (6) continuously monitor the quality of water bodies.

Abstract

This study examines the influence of climate change on hydrological processes, particularly runoff, and how it affects managing water resources and ecosystem sustainability. It uses CMIP6 data to analyze changes in runoff patterns under different Shared Socioeconomic Pathways (SSP). This study also uses a Deep belief network (DBN) and a Modified Sparrow Search Optimizer (MSSO) to enhance the runoff forecasting capabilities of the SWAT model. DBN can learn complex patterns in the data and improve the accuracy of runoff forecasting. The meta-heuristic algorithm optimizes the models through iterative search processes and finds the optimal parameter configuration in the SWAT model. The Optimal SWAT Model accurately predicts runoff patterns, with high precision in capturing variability, a strong connection between projected and actual data, and minimal inaccuracy in its predictions, as indicated by an ENS score of 0.7152 and an R² coefficient of determination of 0.8012. The outcomes of the forecasts illustrated that the runoff will decrease in the coming years, which could threaten the water source. Therefore, managers should manage water resources with awareness of these conditions.

Abstract

Irrigation management is essential for addressing global food security challenges under changing climate. This review discusses the integration of advanced irrigation technologies and their roles in enhancing water use efficiency and managing energy demands within agricultural systems. High-efficiency irrigation systems, such as drip and sprinkler systems, have significant potential to reduce water use and increase crop yields. However, their adoption varies worldwide, and the efficiency of existing irrigation practices often remains inadequate, resulting in substantial water losses due to outdated management practices. Emerging technologies and innovative irrigation strategies, including precision agriculture and advanced crop models, provide promising pathways for improving irrigation efficiency. Nonetheless, the widespread integration of these technologies is hindered by high costs, the need for technical expertise, and challenges in adapting existing agricultural systems to new methodologies. Irrigation systems can have substantial energy requirements, particularly those dependent on groundwater. The exploration of the water-environment-energy-food (WEEF) nexus illustrates the importance of a balanced approach to resource management, which is crucial for achieving sustainable agricultural outcomes. Future research should include lowering barriers to technology adoption, enhancing data utilization for precision irrigation, promoting integrated management strategies within the WEEF framework, and strengthening policy support for sustainable practices. This review proposes a multidisciplinary approach to irrigation management that includes technological innovation, strategic policy development, and global cooperation to secure sustainable agricultural practices and ensure global food supply resilience in the face of climate change.

Abstract

Runoff from glaciated catchments is an integrated process that includes glacier melt, snowmelt, rainfall and surface and subsurface runoff of meltwater from glacierized and non-glacierized areas. Monitoring and quantifying the contribution of the hydrologic components (snow, ice and rain) to river discharge in the Himalayan basins is essential for decision-making in the water sector, particularly in water resources management and flood risk reduction in the region. An attempt has been made to characterize and hydrologically model streamflow (Bhagirathi River) for the Gangotri Glacier (Central Himalaya, India). A semi-distributed conceptual hydrological model is used for the streamflow modelling and assessing the major streamflow components (snow melt, glacier melt and rainfall runoff). Initially, the model was calibrated using the available in situ hydro-meteorological records for the ablation seasons of 2013–14 to 2015–16 (3 years), and further validated for the ablation seasons of 2016–17 to 2018–19 (3 years). The model performed well for all the studied years except for some months, where abrupt changes in the contrasting weather parameters (precipitation and temperature) were recorded. In the Gangotri Glacier Valley (upper Bhagirathi River catchment), snowmelt contributed the largest portion (55.5%) to total streamflow followed by glacier melt (29.7%) and rainfall runoff components (14.7%).