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Climate change exerts significant impacts on ecosystems through extreme climate events (ECEs), which are linked to various climate variables and can occur in different seasons. However, previous studies predominantly focus on a single type of ECE within specific seasons. We address this research gap by examining four typical types of ECEs: droughts, pluvials, heatwaves, and coldwaves, which are consistently defined and identified on a daily scale using a recently proposed statistical method. Butterflies in England serve as our study subject since their life stages occur throughout the year, and we had access to a 45-year dataset encompassing 57 butterfly species. First, we analyzed the correlation between abundance changes and the severity of ECEs, revealing varying sensitivity to ECEs across different life stages. Notably, abundance changes are negatively correlated with the severity of heatwaves in the hibernation, egg, and larval stages, as well as with the severity of pluvials in the larval, pupal, and adult stages. Second, we identified the most extreme climate events (MECEs) in England in the period from 1950 to 2020 and linked them with synchronized abundance changes for all species. MECEs in the sensitive stages of butterflies are frequently (for eight out of 11 MECEs) associated with synchronized decreases in their abundance. Our findings underscore the importance of considering diverse types of ECEs across all seasons to gain insights into their potential ecosystem effects. We draw attention to the fact that our analyses are primarily data-driven, with limited ecophysiological interpretation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The BELSAR dataset consists of high-resolution multitemporal airborne mono- and bistatic fully-polarimetric synthetic aperture radar (SAR) data in L-band, alongside concurrent measurements of vegetation and soil biogeophysical variables measured in maize and winter wheat fields during the summer of 2018 in Belgium. Its collection was funded by the European Space Agency (ESA) to address the lack of publicly-accessible experimental datasets combining multistatic SAR and in situ measurements. As such, it offers an opportunity to advance the development of SAR remote sensing science and applications for agricultural monitoring and hydrology. This paper aims to facilitate its adoption and exploration by offering comprehensive documentation and integrating its multiple data sources into a unified, analysis-ready dataset., (© 2024. The Author(s).)

Understanding irrigation expansion and its implications on water availability is critical for development of decision support tools for sustainable water resources planning. Irrigation expansion in the Lake Tana Basin of Ethiopia from 1986 to 2016 was examined using image classification from thematic maps and remote sensing imagery (Landsat5-8 TM/ETM+, OLI). The soil-adjusted vegetation index (SAVI) imagery was used to identify irrigated cropland based on greenness. Irrigation water consumption was determined by calculating irrigation water requirements of the major crops grown with irrigation and using the estimated irrigated land size of the basin. Irrigated croplands have increased in area from 55 to 65 % in the last past 30 years (from 1986 to 2016). The classification obtained is accurate to the level of 89.6% and Kappa coefficient of 0.83. The Landsat imagery is useful in tracking of the spatiotemporal patterns of irrigated croplands for water management purposes, especially in data deficient areas dotted sparsely by small holder irrigated farms. The results indicated that irrigation consumption in the basin increased from 0.380 km 3  year -1 in 1986 to 0.798 km 3  year -1 in 2016. It is found that the available water cannot sustain the projected irrigation expansion unless alternative water sources are found. Also, more precise irrigation technologies have to be implemented to minimize water losses.

In just the past five years, the field of Earth observation has progressed beyond the offerings of conventional space agency based platforms to include a plethora of sensing opportunities afforded by CubeSats, Unmanned Aerial Vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically on the order of one billion dollars per satellite and with concept-to-launch timelines on the order of two decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturise sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist five years ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of the cost of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-meter resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen-scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the Internet of Things as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilise and exploit these new observing systems to enhance our understanding of the Earth and its linked processes.