• Energy Research

Understanding the driving factors for precipitation extremes matters for adaptation and mitigation measures against the changing hydrometeorological hazards in Yangtze River basin, a habitable area that provides water resources for domestic, farming, and industrial needs. However, the region is naturally subject to major floods linked to monsoonal heavy precipitation during May–September. This study aims to quantify anthropogenic influences on the changing risk of 2-week-long precipitation extremes such as the July 2019 extreme cases, as well as events of shorter durations, over the middle and lower reaches of Yangtze River basin (MLYRB). Precipitation extremes with different durations ranging from 1-day to 14-days maximum precipitation accumulations are investigated. Gridded daily precipitations based on nearly 2,400 meteorological stations across China are used to define maximum accumulated precipitation extremes over the MLYRB in July during 1961–2019. Attribution analysis is conducted by using the Met Office HadGEM3-GA6 modeling system, which comprises two sets of 525-member ensembles for 2019. One is forced with observed sea-surface temperatures (SSTs), sea-ice and all forcings, and the other is forced with preindustrialized SSTs and natural forcings only. The risk ratio between the exceedance probabilities estimated from all-forcing and natural-forcing simulations is calculated to quantify the anthropogenic contribution to the changing risks of the July 2019–like precipitation extremes. The results reveal that anthropogenic warming has reduced the likelihood of 2019-like 14-days heavy precipitation over the mid–lower reaches of the Yangtze River by 20%, but increased that of 2-days extremes by 30%.

The aim of this work is to update ground‐clutter classification methods in weather radar rainfall measurements to more accurately identify clutter pixels from wind farms. Measurements from two dual‐polarised weather radars, based in the United Kingdom, will be used to determine the characteristics of multiple wind farms in the North Sea and the Irish Sea. Currently 21 of the top 25 largest offshore wind farms are located in these regions. The extensive area occupied by the wind farms creates problems for weather radars located in the neighbouring European countries. Datasets of wind‐farm, precipitation and ground‐clutter pixels were aggregated from Thurnham Radar measurements to form novel membership functions that can be used in a fuzzy logic classification system to identify wind‐farm clutter. When only ground‐clutter datasets were used for classification, areas of the radar scans taken up by wind‐farm clutter were misclassified as rainfall. The inclusion of wind‐farm measurements led to an increase in the ability of the algorithm to detect these pixels as clutter, as the Heidke Skill Score increased from 67.4 to 97.8%. However there was a slight increase in the number of precipitation pixels incorrectly classified as clutter, with the false alarm rate increasing from 0.05 to 1.24% when all variables are used. The algorithm performed slightly better when applied to another radar on Hameldon Hill, showing promise for application to the UK network without recalibration of membership functions.

Abstract The widely used design storms in urban drainage networks has different drawbacks. One of them is that the shape of the rainfall temporal pattern is fixed regardless of climate change. However, previous studies have shown that the temporal pattern may scale with temperature due to climate change, which consequently affects peak flow. Thus, in addition to the scaling of the rainfall volume, the scaling relationship for the rainfall temporal pattern with temperature needs to be investigated by deriving the scaling values for each fraction within storm events, which is lacking in many parts of the world including the UK. Therefore, this study analysed rainfall data from 28 gauges close to the study area with a 15-min resolution as well as the daily temperature data. It was found that, at warmer temperatures, the rainfall temporal pattern becomes less uniform, with more intensive peak rainfall during higher intensive times and weaker rainfall during less intensive times. This is the case for storms with and without seasonal separations. In addition, the scaling values for both the rainfall volume and the rainfall fractions (i.e. each segment of rainfall temporal pattern) for the summer season were found to be higher than the corresponding results for the winter season. Applying the derived scaling values for the temporal pattern of the summer season in a hydrodynamic sewer network model produced high percentage change of peak flow between the current and future climate. This study on the scaling of rainfall fractions is the first in the UK, and its findings are of importance to modellers and designers of sewer systems because it can provide more robust scenarios for flooding mitigation in urban areas.

The Nile Delta has been suffering from complex environmental hazards caused by climate change and human-induced evolvements, which have led to adverse impacts on national food security. An unfavourable nexus between solid waste management issues and extreme hydrological events is examined mainly through extensive field investigation and literature research, which is an emerging issue affecting food safety and security whilst still being overlooked so far. The findings not only reveal the significance of the emerging issue but also support our proposed recommendations in the policy/legislation and technology sphere. This interdisciplinary research employs a holistic lens that covers diverse perspectives, including systemic problems, wastewater treatment, and environmental neuroscience, to explore the relationship between food, climate change, water management, and waste pollution, and to achieve novel discoveries for the practical adaptations of Egypt’s challenges.

Abstract Although most phenology models can predict vegetation response to climatic variations, these models often perform poorly in precipitation-limited regions. In this study, we modified a phenology model, called the Growing Season Index (GSI), to better quantify relationships between weather and vegetation canopy dynamics across various semi-arid regions of Iraq. A modified GSI was created by adding a cumulative precipitation control to the existing GSI framework. Both unmodified and modified GSI values were calculated daily from 2001 to 2010 for three locations in Eastern Iraq: Sulaymaniyah (north), Wasit (central) and Basrah (south) and a countrywide mean and compared to the Normalized Difference Vegetation Index (NDVI) from MODerate-resolution Imaging Spectroradiometer (MODIS) for the same time period. Countrywide median inter-annual correlations between GSI and NDVI more than doubled with the addition of the precipitation control and within-site correlations also show substantial improvements. The modified model has huge potential to be used to predict future phenological responses to changing climatic conditions, as well as to reconstruct historical vegetation conditions. This study improves our understanding of potential vegetation responses to climatic changes across Iraq, but it should improve phenological predictions across other semi-arid worldwide, particularly in the face of rapid climate change and environmental deterioration.