We developed a data-driven prediction system for precipitation (Pr) and near-surface temperature (T2m) over the global domain by utilizing NASA’s satellite observations and the associated reanalysis products, with the focus on S2S hydrologic prediction. Our approach is based on a well-established methodology of linear inverse modeling modified and adapted by our science team for high-resolution modeling of precipitation. The key element of this new methodology is the usage of a so-called pseudo-precipitation (PP) variable, equal to the actual Pr in the case the precipitation is occurring and, otherwise, equal to the (negative) air-column integrated water-vapor saturation deficit (the amount of water vapor to be added to the air column to achieve saturation at each vertical level). The model Pr and T2m forecasts are then validated against the observed fields as usual. It is in part through the T2m modeling that the S2S predictability associated with low-frequency dynamical climate modes would filter in the potential S2S precipitation prediction skill within the proposed framework. The system above was shown to be an efficient tool for simulating independent sequencies of daily evolution of the global T2m and Pr fields with spatiotemporal characteristics strikingly similar to the observed characteristics. We are currently evaluating its predictive capabilities. We hypothesize that: (i) the resulting data-driven prediction system may rival the S2S skill of state-of-the-art NWP models in predicting extreme events in widely diverse precipitation environments; and that (ii) it will do so due to its ability to capture tropical low-frequency modes, as well as tropical-to-extratropical teleconnections in the context of a globally connected, seamless multi-scale model. Our goals are to demonstrate both the proposed model skill and its internal dynamical sources, as well as to map out the ensuing implications of these results for hydrologic predictions throughout the globe.

On 10 May 2024, a powerful coronal mass ejection arrived at Earth at 17:05UT and caused a major geomagnetic storm. With the minimum SYM-H excursion of-497 nT (5-min data), this storm is the largest geomagnetic disturbance since March 1989, and can be categorized as a superstorm. In this work, by using a set of ground-based and space-borne instruments, we study the electrodynamic and ionospheric response to the May 2024 storm at middle and low latitudes. During the main phase of the storm, we observed a major super-fountain effect associated with an extreme ionospheric uplift. During the maximum disturbance at ~23:30UT on 10 May, the amplitude within the equatorial ionization anomaly crests reached 170 TECU in GNSS data and 130 TECU in the Swarm A data (i.e., above ~430 km of altitude). Moreover, DMSP showed the occurrence of a 2-peak density structure at ~850 km of altitude with 500-600% density increase, which is an indication of an extremely strong upward ExB drift and strong meridional thermospheric winds circulating during this storm. While the observed dayside ionospheric effects are quite significant, they are less intense than those that occurred during the 15 July 2000 and 29-30 October 2003 superstorms. During the recovery phase, a severe negative storm was observed in the American sector, driven by very strong ExB drift and composition changes. The negative deviation occurred during the recovery phase of the May 2024 storm is one of the strongest ever observed since the beginning of the GPS/GNSS VTEC era. Key-points:-during the main phase of the storm, an extreme dayside ionospheric uplift occurred driven by the PPEF and by storm-time thermospheric winds-the recovery phase was marked by significant drops in the ionospheric plasma density driven by downward ExB drift-compared to previous superstorms, the May 2024 event caused moderately intense positive ionospheric storm and the strongest negative storm

Arctic infrastructure is challenged by ice-rich permafrost thaw that causes differential ground subsidence. Economic impact estimates of permafrost thaw damages require accurate infrastructure inventories. We developed a deep learning-based mapping pipeline, HABITAT (High-resolution Arctic Built Infrastructure and Terrain Analysis Tool), to automatically detect infrastructure from Maxar satellite imagery in 285 Alaskan communities. Combining HABITAT with OpenStreetMap, we mapped a building footprint of 53M m2 and a road network of 50,477 km across Alaska. HABITAT adds 17M m2 to the statewide building footprint not accounted for by OpenStreetMap and 6M m2 within discontinuous and continuous permafrost. We identified infrastructure at risk of damage from bearing capacity loss and ground subsidence between the decades 2015–2024 and 2055–2064 projected by a permafrost geotechnical model. Estimated damages to buildings and roads could cost Alaska $37B to $51B under the SSP245 and SSP585 scenarios, respectively. This is $20 to $48B more than estimates from previous literature due to the additional building footprint mapped by HABITAT.

The Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model is an integrated system of physics-based hydrologic process models. This approach of using a system of physics-based models allows GSSHA to be a good fit for many watershed management decision support applications, such as understanding changing flood dynamics from urban growth as well as using natural and nature-based solutions, such as wetlands or artificial beaver dams, to ameliorate flooding potential. One of the key roles GSSHA has played is modeling compound coastal storm flooding – extreme rainfall combined with storm surge from tropical systems. Simulating such events requires coupling of overland, riverine, coastal, and weather modeling systems. A challenge facing the USACE modeling community is enabling the programmatic connections between models to reduce manual input errors and increase the throughput in time-sensitive situations. In this talk we will discuss how GSSHA has been modernized to enable programmatic-level access to GSSHA. This allows GSSHA to be used in systems of computational models as well as opens up GSSHA to a wide level of data science integration applications, particularly as a part of the NextGen Framework.

A document by Kostas Leptokaropoulos. Click on the document to view its contents.

Classically, routing in Land Surface Models (LSMs) has been achieved using simple schemes with relatively coarse spatial resolution, such the 0.5-degree TRIP model and its derivatives. However, moving to higher resolution requires us to re-think this approach, but at the expense of increasing complexity and computational cost. Some clues to what these new methodologies might be can potentially be found in the field of global flood inundation modelling. Here, over the last ten years, a set of methods have been developed to map areas at risk of flood inundation at high (now 1 arcsecond) resolution over the entire land surface using full 2D hydrodynamic methods. Such approaches are likely still too costly to implement directly in Land Surface Models, but a consideration of findings from these studies can indicate the essential elements that would need to be included in more a lightweight approach.

Prado Dam, located near Los Angeles California, is used to control flows for water supply and flood control in the Sana Ana River with a basin area of 6,216 km2. Existing operations at Prado Dam are supported by streamflow forecasts issued by the California Nevada River Forecast Center (CNRFC). The CNRFC uses a well-tested set of semi-lumped models developed in the 1970s to simulate and predict flows upstream of Prado as well as Prado reservoir inflows. The models are calibrated to current watershed conditions and executed in a modern forecasting framework. Nonetheless, there remains a potential to improve streamflow forecasts using a contemporary physics-based, gridded hydrologic model that can be coupled with a mesoscale weather forecast model run on a similar scale (West-WRF), which may perform better in an arid region like the Santa Ana River Watershed where there are a limited number of events for model tuning. Additional benefits for reservoir operations may be derived from integrated simulation of the watershed, streams, and reservoir. To test this hypothesis, the Santa Ana River watershed and Prado Dam were simulated with the Gridded Surface Subsurface Hydrologic Analyses (GSSHA) model, as an alternative to other watershed models such as those used by the CNRFC. The model is calibrated/verified to observed streamflows and changes in reservoir volume using observed rainfall data, then tested against observed reservoir inflows for an extended simulation period. Utilizing changes in reservoir volume in the calibration allowed for better estimates of reservoir volume without significantly deteriorating flow estimates. The GSSHA model, developed for larger flood events, outperformed the current system for larger events, while performing comparable for other events, indicating it might provide additional information in support of reservoir operations An experimental operational model was developed to run on the Center for Western Weather and Water Extremes (CW3E) computer resources using a West-WRF forecast; output is downloaded to and displayed on the US Army Corps of Engineers UMIP system, which shows all types of measured and simulated hydrologic data online. Pending funding, this system could be operationalized to provide additional reservoir operational guidance, especially during extreme events.

We have developed a novel method for retrieving the single-point lower ionospheric density profile, utilizing observations from Loran-C and a ray-theory-based Earth-Ionosphere WaveGuide (RT-EIWG) model. Compared to other signals probing the D layer, such as VLF transmitters which provide path-averaged information on the upper boundary, and lightning sferics, which are less stable and dependent on the unpredictable occurrence of lightning, Loran-C offers significant advantages. The reflections of the Loran-C signal are clearly resolved over distances of 300-600 km. Additionally, the 1-hop reflection does not overlap with subsequent hops, and the signal is consistently emitted along a fixed path (interval ranges from 40 to 100 milliseconds). The RT-EIWG model effectively simulates ionospheric reflections at distances less than 1000 km. By combining the RT-EIWG model with Loran-C observations, we have established a new method for determining the electron density profiles of the D layer. Initial results indicate that the nighttime lower ionosphere varies with a period of a few minutes consistently. The method would be a powerful tool for the research of lower ionosphere interaction in the future.

The Isocon method is a widely used and efficient approach for mass-transfer calculations. However, it faces significant challenges, including the reliable identification of immobile elements, selection of parental source compositions, and effective visualization. This paper aims to establish a general principle for selecting reference immobile elements. It demonstrates that the Isocon diagram cannot guarantee the accurate identification of real immobile elements and clarifies the meanings of ∆𝐶𝐶 𝑚𝑚 and ∆𝐶𝐶 𝑚𝑚 𝐶𝐶 𝑚𝑚 𝑂𝑂. Importantly, mobile elements with identical mobility align along the same line in an Isocon plot, complicating the selection of immobile elements. For immobile elements, ∆𝐶𝐶 𝑚𝑚 and ∆𝐶𝐶 𝑚𝑚 𝐶𝐶 𝑚𝑚 𝑂𝑂 both equal zero, and their partition coefficients approach infinity. This study provides alternative masstransfer calculation methods without requiring knowledge of original concentrations, based on the equation ∆𝐶𝐶 𝑚𝑚 = (𝜀𝜀 − 1) 𝐶𝐶 𝑚𝑚 𝐴𝐴 𝐷𝐷 𝐴𝐴 dis = (𝜀𝜀 − 1)𝐶𝐶 𝑚𝑚 𝑑𝑑𝑑𝑑𝑑𝑑 = (𝜀𝜀 − 1)𝐶𝐶 𝑚𝑚 𝐴𝐴 𝐷𝐷 𝑑𝑑𝑑𝑑𝑑𝑑/𝐴𝐴. The principle for selecting immobile elements is to prioritize those with partition coefficients closest to infinity. To validate the proposed approach, datasets from magmatic, metamorphic, ore-forming, and theoretical environments were analyzed. The results confirm the approach's reliability and emphasize the critical role of high-quality chemical analyses in mass-transfer studies.

State estimation is a critical task in practice as it is the prerequisite for effective parameter estimation (PE), skillful prediction, optimal control, and the development of robust and complete datasets. Of essence is the probabilistic state estimation of the unobserved variables in high-dimensional complex nonlinear turbulent dynamical systems with intermittent instability, given a partially observed time series. Data assimilation (DA) through Bayesian inference combines the partial observations and the model-induced likelihood, potentially plagued by a small error in model structure or initial condition, to form the posterior distribution for optimal state estimation. In this work, an efficient algorithm with closed-form explicit expressions is developed for the optimal (in the mean-squared sense) online smoother and sampling of a rich class of nonlinear turbulent dynamical systems widely appearing in modeling natural phenomena like physics-constrained stochastic models (noisy Lorenz models, low-order models of Charney-DeVore flows, paradigm models for topographic mean flow interaction), stochastically coupled reaction-diffusion models in neuroscience and ecology (stochastically coupled FitzHugh-Nagumo models, stochastically coupled SIR epidemic models), multiscale models for geophysical flows (noisy Boussinesq equations, stochastically forced rotating shallow water equation), and to develop realistic systems for the Madden-Julian oscillation and Arctic sea ice. The conditional Gaussian nonlinear system framework is highly flexible since it encompasses the linear Kalman filter and the Rauch-Tung-Striebel smoother and facilitates studying extreme events, stochastic parameterisation, DA, and online PE. The method particularly handles systems with hidden intermittency and extreme events along with the associated highly non-Gaussian features, such as heavy tails in the probability density functions, by using a computationally effective and systematic method to determine the adaptive lag for the online procedure. A rigorous analysis of the performance of the framework will be illustrated through numerical simulations of high-dimensional Lagrangian DA and online model identification of highly intermittent time series via an expectation-maximization procedure.

Existing near-real-time (NRT) SAR-based flood mapping systems are typically designed for individual SAR sensors and often face challenges in achieving reliable flood extent delineation across diverse environments. For instance, the self-supervised Radar-based Inundation Daily (RAPID) system (Shen et al., 2019a) struggled with sensitivity to snow and ice. In this study, we enhanced RAPID by integrating an Adaptive Initialization (AdaI) module for refining initial water samples. This enhanced system is designed to work with multiple SAR imagery in an NRT manner, adapting to various configurations, including variable microwave wavelength. This flexibility significantly improves the system's ability to detect submerged floods in complex conditions,especially in complex regions such as mixed snow cover and arid dryland areas, so can enhance the temporal coverage and accuracy. We demonstrate the system's efficacy through a comparative analysis against baseline methods, including a simple bimodality-based segmentation and the original RAPID, showing balanced performance across diverse environments and consistent detection capabilities. Moreover, the system can seamlessly work across various SAR sensors. The input SAR imagery ranges from Sentinel-1, Radar Constellation Mission (RCM), Radarsat-2 (RS2), Capella, and Advanced Land Observing Satellite-2 (ALOS-2), which include C-band, X-band, and L-band, respectively. Results align well with very high-resolution PlanetScope images and reference floodwater masks, highlighting that the robustness of SAR flood mapping critically depends on the initial sample generation, as misleading initializations can significantly compromise detection accuracy to the extent that post-processing cannot adequately correct. Furthermore, we outline future developments for NRT flood mapping systems using multi-SAR sensors, emphasizing the integration of advanced change-detection algorithms and the expansion of satellite tasking to improve detection performance and response times.

In agricultural areas, Tile Drains (TDs) are often installed by farmers in order to drain any excess of water accumulating in crop fields. This anthropogenic modification to the land surface can have strong effects on streamflow in these areas. Here, a simple technique was employed in order to partly account for the effect that TDs can have on streamflow simulated with the GEM-Hydro physically-based and distributed hydrologic model developed at Environment and Climate Change Canada (ECCC). The technique consists in significantly increasing the horizontal hydraulic conductivity of the soil layer generally containing the TDs, in the land-surface scheme of GEM-Hydro, for the part of the grid-cell that contains TDs. To do so, a multiplying coefficient obtained through automatic calibration was used to increase the appropriate soil layer’s horizontal hydraulic conductivity. The part of the grid-cell containing Tile Drains was obtained from different databases depending on the country (i.e., US or Canada) or the province (Ontario or Quebec). Moreover, a similar strategy was followed to represent the effect that agricultural ploughing practices can have on streamflow, by increasing the model’s vertical hydraulic conductivity for the superficial soil layers. The methodology employed allowed to significantly increase the performance of GEM-Hydro streamflow simulations in the watershed of the Laurentian Great-Lakes when compared to the default (current) version of the model, while maintaining similar performances for other hydrologic variables simulated with GEM-Hydro, such as evapotranspiration, and soil moisture and surface temperature simulations, when comparing for example to the recent Global Land Evaporation Amsterdam Model (GLEAM version 3.5b) reference dataset. These findings are promising in the view of developing land-surface schemes that can be applied both for two-way coupling with atmospheric models and for environmental and hydrologic applications.

Rapid expansion of geological storage of CO2, at the giga-tonne scale, is required to mitigate CO2 emissions into the atmosphere. Potential storage basins need to be characterized rapidly to determine the best sites for injection and long-term security. The hydrocarbon industry has developed the common risk segment mapping approach over many years, for assessing exploration potential based on static reservoir properties (geological seals, permeability, porosity etc). More recently this has also been applied to evaluating storage potential for CCS by Bump et al. (2021).

In 2016, the establishment of an ARC Critical Zone Observatory (CZO) within the TERENO network intensified research efforts. The CZO is equipped with a comprehensive monitoring system, including wireless sensor networks, cosmic-ray neutron sensors, piezometers, meteorological stations, and additional tools for hydro-geophysical surveys, stable isotope analysis, and remote sensing. Coupled with in-situ soil hydraulic experiments and modeling, the ARC has evolved into a unique synthesis of a CZO and an open-air laboratory. Our primary goal is to quantify the impacts of Mediterranean climate variability and land use change on water availability and management within the ARC. By integrating field observations with advanced modeling techniques, we aim to provide crucial insights for sustainable water resource management in this vulnerable region. Nasta et al. (2023HP)

Heatwave (HW) events are expected to worsen due to climate change, resulting in increased societal impacts such as human illness, crop failures, wildfires, power outages, infrastructure disruption, and damage. The southwest region of Bangladesh, particularly the Khulna division, is anticipated to experience severe consequences of climate change, with increased frequency and intensity of HWs causing numerous deaths due to heatstroke. To understand and mitigate the severity of HWs in Khulna, various aspects were analyzed, such as frequency, duration, and magnitude. This involved calculating the yearly number of HW events (HWN), the yearly sum of HW days (HWF), the length of the longest yearly event (HWD), the average size of all yearly events (HWL), the hottest day of the hottest yearly event (HWA), and the average magnitude of all yearly events (HWM). This analysis used daily maximum temperature records from four observation stations during the summer months (March to May or MAM) from 1990 to 2014, as well as data from thirteen CMIP6 models for three time periods: historical (1990-2014), near future (2026-2050), and far future (2076-2100) for two emission scenarios SSP2-4.5 and SSP3-7.0. Observation data were used to correct the bias of CMIP6 models using empirical quantile mapping (EQM) and to evaluate these models to characterize HW characteristics. HW events were identified using the 90th and 75th percentiles of the distribution of daily maximum temperature values for the reference period. Based on the Pearson Correlation Coefficient (PCC), the values of indices from GCMs are not strongly correlated with the values from the observed dataset. Additionally, indices like HWA and HWM are calculated from GCMs, which are significantly less correlated with the results from the observed dataset, that denotes GCMs cannot project extreme values for particular HW events . The frequency, duration, and magnitude of HWs showed abrupt changes for both emission scenarios compared to the historical period. The historical pattern of HW indices of the Khulna Division has not changed much over 25 years except HWF. HWs are proving to be the deadliest disaster in modern existence. Therefore, a comprehensive understanding of the characteristics of HWs in a particular area is crucial for raising awareness and potentially saving many lives.

We employ differential adjoint tomography to image the San Gabriel Basin, Chino Basin and San Bernardino Basin in the northern Los Angeles to suppress the effects of unknown noise sources. We obtain Empirical Green’s Function of Love wave with high Signal-to-Noise Ratio. A region wise optimization strategy is implemented to address regional sensitivity kernel incompatibility and thus improve resolution in the region with suboptimal data quality. Our results align well with known structures and reveal more details than previous studies in this region. Beyond delineating basin structures, we identify a high-velocity channel at the San Jose Fault. Diffuse low-velocity anomalies detected at the Fontana seismicity region provide evidence for fluid migration. Lastly, we identify a concentrated low-velocity anomaly between 0.8 km to 4 km depth beneath Puddingstone Reservoir, potentially indicating groundwater seepage, which may influence seismicity in the area.

Variations in stable oxygen isotopic compositions in sea ice provide information on environmental conditions during sea ice formation, and also are important in understanding regional and temporal aspects of the freshwater budget of the Arctic Ocean. We analyzed the oxygen isotope fractionation between sea ice and seawater using ice core and surface ocean samples obtained in a field study in the Lincoln Sea/Switchyard region of the Arctic Ocean. Using the Sea Ice Tracking Utility (SITU) we track the sea ice backward in time along drift trajectories, and use a simple model to calculate ice growth rates. Our results indicate that sea ice at the bottom of the floes that we sampled in the Switchyard Region grew within the past winter along a trajectory extending back to the North Pole. The effective fractionation coefficients from the bottom ice layers and the parent water mass are close to 2.11 ‰ with a standard error of ± 0.06 ‰. Knowing this sea ice oxygen isotope fractionation coefficient for high Arctic drifting ice is critical for use of equations for mass balance, salinity, oxygen isotopes and nutrients to calculate water mass fractions and sources to understand freshwater balance.

Haoran Liu1, Fa Li2, Hamid Dashti1, Fujiang Ji1, Min Chen1 *1 Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, WI, USA2 Department of Earth System Science, Stanford University, CA, USA*Corresponding author: Min Chen ([email protected])