This study analyzes the climatology of Large Scale Traveling Ionospheric Disturbances (LSTIDs) over Europe from 2014 to 2023 using the HF-Interferometry method (HF-INT), which provides LSTID's activity detected in near-real-time by a network of European ionosondes. For this purpose, this LSTID activity has been analyzed in depth and a Catalogue of observed LSTID events has been obtained providing, among others, the onset time, duration, dominant period, and propagation velocity vector of each LSTID event. The results, derived from this Catalogue of LSTIDs, reveal that LSTID occurrence is significantly dependent on local time, seasonal variations, and geomagnetic conditions, with activity peaks observed during equinoxes, particularly at night and in the early morning hours. Key propagation characteristics include velocities ranging from 500 to 700 m/s and azimuths, suggesting a southward direction, closely associated with auroral activity. Additionally, a distinct westward propagation in the morning hours is attributed to the solar terminator effect. These findings highlight the complex interactions between ionospheric dynamics and geomagnetic conditions, offering new insights into the mechanisms governing TID generation and propagation.

A document by Chetraj Pandey. Click on the document to view its contents.

A document by Jaclyn Clement Kinney. Click on the document to view its contents.

Methodology Conclusions and Future work Results This study investigates the impacts of climate change and increasing water demand on water stress levels in the Apalachicola-Chattahoochee-Flint (ACF) River Basin, a critical transboundary water system shared by Georgia, Alabama, and Florida. Recognized as a compound natural hazard, drought in this region is exacerbated by climatic shifts and human-driven policy changes. By analyzing compound drought indices such as SPI, MSSRI, and PDSI, the study explores the interplay between drought events and water management policies, highlighting the influence of state and federal decisions. Results emphasize the need for integrated policy frameworks to address compounded water deficits and mitigate transboundary conflicts.

National goals frequently require transdisciplinary or multisectoral science and policy coordination, making them challenging to achieve. The One Health approach is designed to coordinate and communicate science and policy across multisectoral and transdisciplinary challenges by integrating science and policy for the human, animal, plant, and environmental health sectors. This approach is expanding to address a larger spectrum of societal, economic, national security, and environmental challenges. The Science and Technology Policy Institute (STPI) asked if the application of a One Health approach would improve our ability to achieve national goals.

Arizona State University, Earth Genome, and USACE' Engineer Research and Development Center has been working together under a Network for Engineering With Nature project to develop a Web Application tool for regional screening of Managed Aquifer Recharge (MAR) Projects. The objective of the project is to provide relative, regional indices for appropriate siting of MAR, which would be an innovative approach to flood management and restore hydrological and ecological processes linked to aquifer recharge. Developing a rigorous decision-making framework includes physical characteristics such as surface water runoff, connection with groundwater aquifers; as well as social considerations, such as land use, engaging stakeholders in floodplain management, and distribution of risks and benefits. A suite of analyses, from CONUS-wide Machine Learning for groundwater attributes and statistical estimation of flood volumes to surveys about perceptions of MAR were implemented to inform development of a decision support tool and supplemental guidance in an effort to increase the accessibility of this innovative flood management approach to floodplain managers. This research is funded by the Engineering With Nature ® and Network for Engineering With Nature® programs. https://ewn.erdc.dren.mil/

A document by Kevin Raeder. Click on the document to view its contents.

Submitted To: Urban ClimateG. Aaron Alexander1, Daniel B. Wright1, Carolyn B. Voter2,3, Steven P. Loheide II11Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, Wisconsin, 53704, United States of America2Department of Civil, Construction, and Environmental Engineering, University of Delaware, Newark, Delaware, 19716, United States of America3Department of Earth Sciences, University of Delaware, Newark, Delaware, 19716, United States of AmericaCorrespondence to : G. Aaron Alexander (gaalexander3@wisc.edu)

A document by Kavya Johny. Click on the document to view its contents.

A document by Emily Graham. Click on the document to view its contents.

A document by Kyle Benjamin Heyblom. Click on the document to view its contents.

Soil moisture memory (SMM) is a critical factor in land-atmosphere interactions as it impacts weather and climate predictions. Despite the acknowledged significance of soil hysteresis in soil moisture dynamics, its potential impact on SMM has not been thoroughly studied. This study evaluates the effects of hysteresis on short-term (τS) and long-term (τL) SMM by inverse estimation of soil hydraulic parameters to simulate water flow using HYDRUS-1D with hysteretic and non-hysteretic soil hydraulic properties and a six-year data set from lysimeters in an energy-limited environment in western Germany. Our results show that while hysteresis improves the accuracy of soil moisture simulations, it does not fully capture the dynamics of SMM. Specifically, the model including hysteresis tends to underestimate τS and overestimate τL when compared to observed data. τS is primarily influenced by soil depth, with shallow layers responding rapidly to precipitation, while τL is significantly affected by seasonal variations, peaking in fall and winter due to reduced evapotranspiration and protective grass cover. The positive correlation observed between τS and τL suggests that energy-limited environments promote stable soil moisture profiles through the interaction of vegetation and soil structure. The results obtained suggest, that although soil hysteresis contributes to SMM, it cannot describe the total memory effects even for τS. These findings highlight the need for improved parameterization of soil-vegetation interactions in hydrological models and underscore the importance of validating these models across diverse climatic conditions.

A document by Rachel Atlas. Click on the document to view its contents.

A document by Behshad Mohajer. Click on the document to view its contents.

A document by Shenyue Jia. Click on the document to view its contents.

The Relative Difference (%) between Solar Maximum and Minimum is highest in the UV range. We calculated the shortwave spectral files used in our models with the SOCRATES radiative transfer code. To account for stellar spectral features sensitive to activity, we designed a new band scheme (see the tables) (https://www.met.reading.ac.uk/~yt910424/home/socrates.php) We used SSI data obtained by the LASP TSIS-1 Spectral Irradiance Monitor (SIM) onboard the International Space Station. The data includes measurements taken during at Solar Minimum (averaged from December 1-7/2019, proposed as the reference spectrum for the upcoming CMIP7, as suggested by Funke et al. (2024)) and the Solar Maximum (averaged June 3-20/2024). For wavelengths beyond 2399 nm, both spectra incorporate the same data as the reference spectrum constructed by Coddington et al. (2023).

Environmental models typically rely on stationarity assumptions, which are often violated by process heterogeneity. This paper explores ways to incorporate process heterogeneity into statistical models to improve their performance. It considers problems from different disciplines in environmental sciences and demonstrates the effects of process heterogeneity together with model extensions. The first considered problem addresses process heterogeneity in flood frequency analysis, where flood samples are generated by different processes in catchment and atmosphere. A mixture model that combines peakover-threshold distributions of flood types into an overall distribution can deal with such process heterogeneity. Distributions differ greatly between event types, especially regarding tail heaviness, making this approach highly relevant for flood design. The second problem addresses frequency analysis of minimum flows, where heterogeneity arises from different generation processes in summer and winter. A mixed probability model for minima can adequately incorporate different seasonal distributions into an overall distribution, and a copula-based estimator can additionally incorporate event dependence. The performance gain of the mixed distribution approaches was found to be large, especially for extreme events with a high return period. The third problem addresses the role of process heterogeneity in rainfall models. We propose clustering multiple event characteristics derived from the rainfall series (such as duration and average intensity) to stratify the rainfall series. Partitioning using Gower's distance to include a lightning index enables us to discriminate between convective and stratiform events with different rainfall distributions. The method has potential to improve the realism of rainfall generators. The fourth problem deals with parameter variation in temporal models of environmental variables. It uses daily streamflow series as an example and investigates the effects of process heterogeneity using a tree-based machine learning model. The prediction performance for mean and extreme values changes strongly depending on the quantile loss optimization, and the model parameters also show variation. The results suggest that different or combined models are needed for full time series in the presence of process heterogeneity. The study shows that process heterogeneity can be an obstacle in modeling and should be taken into account from the very beginning of the analysis in an appropriate way. The study should be seen as an encouragement to better understand the statistical assumptions in the models used and to enrich the physical knowledge included in environmental statistics.

A document by Nicole Read. Click on the document to view its contents.