Search

Record-breaking summer forest fires have become a regular occurrence in California. Observations indicate a fivefold increase in summer burned area (BA) in forests in northern and central California during 1996 to 2021 relative to 1971 to 1995. While the higher temperature and increased dryness have been suggested to be the leading causes of increased BA, the extent to which BA changes are due to natural variability or anthropogenic climate change remains unresolved. Here, we develop a climate-driven model of summer BA evolution in California and combine it with natural-only and historical climate simulations to assess the importance of anthropogenic climate change on increased BA. Our results indicate that nearly all the observed increase in BA is due to anthropogenic climate change as historical model simulations accounting for anthropogenic forcing yield 172% (range 84 to 310%) more area burned than simulations with natural forcing only. We detect the signal of combined historical forcing on the observed BA emerging in 2001 with no detectable influence of the natural forcing alone. In addition, even when considering fuel limitations from fire-fuel feedbacks, a 3 to 52% increase in BA relative to the last decades is expected in the next decades (2031 to 2050), highlighting the need for proactive adaptations.

Previous studies have identified a recent increase in wildfire activity in the western United States (WUS). However, the extent to which this trend is due to weather pattern changes dominated by natural variability versus anthropogenic warming has been unclear. Using an ensemble constructed flow analogue approach, we have employed observations to estimate vapor pressure deficit (VPD), the leading meteorological variable that controls wildfires, associated with different atmospheric circulation patterns. Our results show that for the period 1979 to 2020, variation in the atmospheric circulation explains, on average, only 32% of the observed VPD trend of 0.48 ± 0.25 hPa/decade (95% CI) over the WUS during the warm season (May to September). The remaining 68% of the upward VPD trend is likely due to anthropogenic warming. The ensemble simulations of climate models participating in the sixth phase of the Coupled Model Intercomparison Project suggest that anthropogenic forcing explains an even larger fraction of the observed VPD trend (88%) for the same period and region. These models and observational estimates likely provide a lower and an upper bound on the true impact of anthropogenic warming on the VPD trend over the WUS. During August 2020, when the August Complex "Gigafire" occurred in the WUS, anthropogenic warming likely explains 50% of the unprecedented high VPD anomalies.

Damage identification, both in terms of size and location, in bridges is important for timely maintenance and to avoid any catastrophic failure. An earlier experimental study showed that damage in a steel box girder orthotropic plate can be successfully detected using the measured vibration acceleration data. In this study, the wavelet packet decomposition (WPD) method is used to analyze the measured vibration acceleration responses and then the estimation of the permutation entropy (PE) on the re-constructed signals. A damage index is then defined based on the permutation entropy difference (PED) between the damaged and the healthy conditions to detect the location and size of the damage. The method is further validated through the finite element (FE) model of a steel box girder and the computed vibration acceleration responses when subjected to the sinusoidal excitations at different frequencies. In addition, the robustness of the methodology under different white noise interference conditions is also verified. The results show that the proposed methodology can effectively identify the location of human-made damage and accurately estimate the degree of damage under different frequencies of sinusoidal excitation. The method has shown a strong anti-noise property. [ABSTRACT FROM AUTHOR]

The steel rib and the lattice girder are two typical steel arch frames used in the primary lining of the New Austrian Tunnelling Method (NATM) highway tunnel. In the process of tunnel construction, it is necessary to choose the support method according to the mechanical properties, durability, construction difficulty, and economic benefits. In order to analyze the mechanical characteristics of steel arch frames, a loading and measurement test system for steel arch was designed, and destructive tests were carried out on a three-bar lattice girder, four-bar lattice girder, and I-beam steel rib. The steel utilization coefficient was introduced to compare the mechanical properties of the different types of steel frames. The results show that the I-beam steel rib has the largest steel utilization coefficient, but it is not well combined with concrete. Among the lattice girders, the three-bar W-shaped lattice girder uses the least amount of steel under the same load, which reduces the construction cost and is worth applying and popularizing. Additionally, the steel utilization coefficient of different types of lattice girders can be optimized through the ratio of steel bar diameters. It is proved that the steel utilization coefficient can provide a reference for the evaluation and selection of steel frames for the primary lining of highway tunnel engineering. [ABSTRACT FROM AUTHOR]

​​​​​​​Land–atmosphere coupling (LAC) has long been studied, focusing on land surface and atmospheric boundary layer processes. However, the influence of humidity in the lower troposphere (LT), especially that above the planetary boundary layer (PBL), on LAC remains largely unexplored. In this study, we use radiosonde observations from the US Southern Great Plains (SGP) site and an entrained parcel buoyancy model to investigate the impact of LT humidity on LAC there during the warm season (May–September). We quantify the effect of LT humidity on convective buoyancy by measuring the difference between the 2–4 km vertically integrated buoyancy with the influence of background LT humidity and that without it. Our results show that, under dry soil conditions, anomalously high LT humidity is necessary to produce the buoyancy profiles required for afternoon precipitation events (APEs). These APEs under dry soil moisture cannot be explained by commonly used local LAC indices such as the convective triggering potential and low-level humidity index (CTP / HI Low), which do not account for the influence of the LT humidity. On the other hand, consideration of LT humidity is unnecessary to explain APEs under wet soil moisture conditions, suggesting that the boundary layer moisture alone could be sufficient to generate the required buoyancy profiles. These findings highlight the need to consider the impact of LT humidity, which is often decoupled from the humidity near the surface and is largely controlled by moisture transport, in understanding land–atmospheric feedbacks under dry soil conditions, especially during droughts or dry spells over the SGP. [ABSTRACT FROM AUTHOR]

Land-atmosphere coupling (LAC) has long been studied focusing on land surface and atmospheric boundary layer processes. However, the influence of lower tropospheric (LT) humidity on LAC remains largely unexplored. In this study, we use radiosonde observations from the U.S. Southern Great Plains (SGP) site and an entrained parcel buoyancy model to investigate the impact of LT humidity on LAC there during the warm season (May–September). We quantify the effect of LT humidity on convective buoyancy by measuring the difference between the 2–4 km vertically integrated buoyancy with and without the influence of background LT humidity. Our results show that, under dry soil conditions, anomalously high LT humidity is necessary to produce the buoyancy profiles required for afternoon precipitation events (APEs). These APEs under dry soil moisture cannot be explained by commonly used local land-atmosphere coupling indices such as the convective triggering potential/low-level humidity index (CTP/HILow), which do not account for the influence of the LT humidity. On the other hand, consideration of LT humidity is unnecessary to explain APEs under wet soil moisture conditions, suggesting the boundary layer moisture alone could be sufficient to generate the required buoyancy profiles. These findings highlight the need to consider the impact of LT humidity, which is often decoupled from the humidity near the surface and largely controlled by moisture transport, in understanding land-atmospheric feedbacks over dry soil conditions, especially during droughts or dry spells over the SGP. [ABSTRACT FROM AUTHOR]

The Wan Jiang Bridge in China was damaged by ship impact and subsequently restored. This paper presents a detailed evaluation of the residual service life (RSL) of the bridge using the reliability analysis method of the Joint Committee on Structural Safety, based on the structural load-bearing capacity (LBC) of the bridge and the design load. Data were collected from bridge design codes, field loading tests and finite-element (FE) analysis. The LBC was initially calculated and subsequently revised using specific coefficients. Using FE analysis, the bending moment on the control section of the bridge was determined under the most unfavourable loading conditions and the ultimate LBC of the bridge was checked. Subsequently, static and dynamic loading tests on the restored bridge were conducted, with the load and capacity determined for the control section based on FE analysis. The loads considered were dead load, vehicle load and crowd load. A time-dependent reliability index was developed for the restored bridge using the probability distributions of capacity and design load variables, and the RSL was determined. This case study, predicting the RSL of the bridge based on a multitude of data, should be valuable for future bridge maintenance and management. [ABSTRACT FROM AUTHOR]

Bridge deflection serves as a vital and intuitive index for the evaluation of bridge safety. Temperature load has the greatest influence on the bridge deformation and studies on the temperature-induced deformation prediction of long-span bridge are in limited numbers. A digital prediction model based on deep learning in minute scale is established to study the bridge deflection caused by temperature. The wavelet transform (WT) is adopted to filter the high-frequency signals of the original deflection caused by the related load factors. Three different networks, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and Transformer variant, are studied and compared in the prediction process. Two different learning strategies considering different input data are also considered to optimize the prediction performance. The proposed prediction model is applied to the temperature induced deflection prediction of a multi-tower double-layer steel truss bridge. The results show that strategy A, which employs temperature time series data as input, is less effective than strategy B. Incorporating both temperature and deflection data as inputs is essential for predicting temperature-induced deflections. Moreover, the Transformer-variant network generally exhibits superior prediction performance compared to the LSTM and Bi-LSTM. The self-attention mechanism of the Transformer allows it to focus on key historical temperature points, thereby enhancing prediction accuracy. [ABSTRACT FROM AUTHOR]

Historically, meteorological drought in the western United States (WUS) has been driven primarily by precipitation deficits. However, our observational analysis shows that, since around 2000, rising surface temperature and the resulting high evaporative demand have contributed more to drought severity (62%) and coverage (66%) over the WUS than precipitation deficit. This increase in evaporative demand during droughts, mostly attributable to anthropogenic warming according to analyses of both observations and climate model simulations, is the main cause of the increased drought severity and coverage. The unprecedented 2020-2022 WUS drought exemplifies this shift in drought drivers, with high evaporative demand accounting for 61% of its severity, compared to 39% from precipitation deficit. Climate model simulations corroborate this shift and project that, under the fossil-fueled development scenario (SSP5-8.5), droughts like the 2020-2022 event will transition from a one-in-more-than-a-thousand-year event in the pre-2022 period to a 1-in-60-year event by the mid-21st century and to a 1-in-6-year event by the late-21st century.