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Abstract Pluvial floods pose a significant threat to properties, yet comprehensive impact analysis is hindered by data limitations on pluvial inundation. To assess pluvial flood impacts, we leveraged U.S. flood insurance claims and policy records for a subset of properties outside 100-year floodplains, streamflow records, and nationwide precipitation data, enabling us to distinguish damage claims caused by pluvial floods over 1978–2021. Strikingly, 87.1% of the claims analyzed from this subset were due to pluvial floods. Utilizing these pluvial flood claims unveiled distinct regional patterns of pluvial impacts across the contiguous U.S. These patterns are informed by the relationship between claim frequency and precipitation within each region. Remarkably, despite the pervasiveness of impacts, many states are seeing declining uptake in pluvial flood insurance coverage. Our study highlights regions facing heightened pluvial flood risks and underscores the critical need for enhanced consideration of pluvial inundation within risk management frameworks. 

This paper studies the allocation of simulation effort in a ranking-and-selection (R&S) problem with the goal of selecting a system whose performance is within a given tolerance of the best. We apply large-deviations theory to derive an optimal allocation for maximizing the rate at which the so-called probability of good selection (PGS) asymptotically approaches one, assuming that systems’ output distributions are known. An interesting property of the optimal allocation is that some good systems may receive a sampling ratio of zero. We demonstrate through numerical experiments that this property leads to serious practical consequences, specifically when designing adaptive R&S algorithms. In particular, we observe that the convergence and even consistency of a simple plug-in algorithm designed for the PGS goal can be negatively impacted. We offer empirical evidence of these challenges and a preliminary exploration of a potential correction. 

Abstract Numerical simulations of Sequences of Earthquakes and Aseismic Slip (SEAS) have rapidly progressed to address fundamental problems in fault mechanics and provide self‐consistent, physics‐based frameworks to interpret and predict geophysical observations across spatial and temporal scales. To advance SEAS simulations with rigor and reproducibility, we pursue community efforts to verify numerical codes in an expanding suite of benchmarks. Here we present code comparison results from a new set of quasi‐dynamic benchmark problems BP6‐QD‐A/S/C that consider an aseismic slip transient induced by changes in pore fluid pressure consistent with fluid injection and diffusion in fault models with different treatments of fault friction. Ten modeling groups participated in problems BP6‐QD‐A and BP6‐QD‐S considering rate‐and‐state fault models using the aging (‐A) and slip (‐S) law formulations for frictional state evolution, respectively, allowing us to better understand how various computational factors across codes affect the simulated evolution of pore pressure and aseismic slip. Comparisons of problems using the aging versus slip law, and a constant friction coefficient (‐C), illustrate how aseismic slip models can differ in the timing and amount of slip achieved with different treatments of fault friction given the same perturbations in pore fluid pressure. We achieve excellent quantitative agreement across participating codes, with further agreement attained by ensuring sufficiently fine time‐stepping and consistent treatment of boundary conditions. Our benchmark efforts offer a community‐based example to reveal sensitivities of numerical modeling results, which is essential for advancing multi‐physics SEAS models to better understand and construct reliable predictive models of fault dynamics. 

Abstract Air temperature (Ta), snow depth (Sd), and soil temperature (Tg) are crucial variables for studying the above- and below-ground thermal conditions, especially in high latitudes. However,in-situobservations are frequently sparse and inconsistent across various datasets, with a significant amount of missing data. This study has assembled a comprehensive dataset ofin-situobservations of Ta, Sd, and Tg for the Northern Hemisphere (higher than 30°N latitude), spanning 1960–2021. This dataset encompasses metadata and daily data time series for 27,768, 32,417, and 659 gages for Ta, Sd, and Tg, respectively. Using the ERA5-Land reanalysis data product, we applied deep learning methodology to reconstruct the missing data that account for 54.5%, 59.3%, and 74.3% of Ta, Sd, and Tg daily time series, respectively. The obtained high temporal resolution dataset can be used to better understand physical phenomena and relevant mechanisms, such as the dynamics of land-surface-atmosphere energy exchange, snowpack, and permafrost. 

Abstract Induced seismicity observed during Enhanced Geothermal Stimulation at Otaniemi, Finland is modeled using both statistical and physical approaches. The physical model produces simulations closest to the observations when assuming rate‐and‐state friction for shear failure with diffusivity matching the pressure build‐up at the well‐head at onset of injections. Rate‐and‐state friction implies a time‐dependent earthquake nucleation process which is found to be essential in reproducing the spatial pattern of seismicity. This implies that permeability inferred from the expansion of the seismicity triggering front (Shapiro et al., 1997,https://doi.org/10.1111/j.1365-246x.1997.tb01215.x) can be biased. We suggest a heuristic method to account for this bias that is independent of the earthquake magnitude detection threshold. Our modeling suggests that the Omori law decay during injection shut‐ins results mainly from stress relaxation by pore pressure diffusion. During successive stimulations, seismicity should only be induced where the previous maximum of Coulomb stress changes is exceeded. This effect, commonly referred to as the Kaiser effect, is not clearly visible in the data from Otaniemi. The different injection locations at the various stimulation stages may have resulted in sufficiently different effective stress distributions that the effect was muted. We describe a statistical model whereby seismicity rate is estimated from convolution of the injection history with a kernel which approximates earthquake triggering by fluid diffusion. The statistical method has superior computational efficiency to the physical model and fits the observations as well as the physical model. This approach is applicable provided the Kaiser effect is not strong, as was the case in Otaniemi.