Description: The extreme hydroclimatic events, such as extreme drought, flood, and drought-flood abrupt alternation (DFAA, defined as a natural phenomenon of sudden wet spells following persistent dry spells), are becoming more frequent and intense in all climatic zones. These hydroclimatic extremes caused by climate change and human activity can pose severe challenges to freshwater ecosystems, which can have significant consequences on the economy and society. For instance, extreme droughts due to insufficient rainfall and/or high temperature would lead to reduced the river flow, slower flow velocity, and decreased dissolved oxygen in water, resulting in increased water pollutants, fish kills, and even the collapse of aquatic ecosystem. The DFAA magnifies the impacts of individual drought and flood events on aquatic ecosystem, which would put excessive strains on the water environment and ecology. Therefore, in this study, it's crucial to investigate the effects of multiple hydroclimatic extremes on the river ecosystem (including the water temperature, dissolved oxygen, nitrogen, phosphorus, and aquatic animals) in order to identify the optimal adaption and mitigation measures. Firstly we identify the different types of extreme hydroclimatic events by adopting widely used indices, such as SPI, SPEI, SRI, etc.. Secondly, applying event coincidence analysis, we explore the correlation, frequency, intensity, and tendency of hydroclimatic extremes. Then by using the copula theory, a two/three dimension joint probabilistic model among indices, water quality, and aquatic animals is proposed.

Description: Pore-scale numerical simulation plays an important role in comprehending water quality. Predicting the permeability of porous media via machine learning models is a crucial aspect of current research in pore-scale modelling. These models can create a mapping relationship between permeability and pore structure, serving as a regression task. However, directly applying 3D data of porous media to classical machine learning models is not possible, requiring the extraction of features from the pore structure. Deep learning models, as an advanced form of classical machine learning, have been successful in permeability prediction using 3D digital images, but come at a high computational cost. To address this, we propose an intuitive feature extraction method that extracts pore structure features from slices of 3D porous media and uses them as input for machine learning models. This not only reduces the amount of input data but also improves training efficiency, while maintaining excellent prediction accuracy. Moreover, due to the spatial continuity of the 2D pore structure features, we achieved improved prediction performance compared to classical machine learning models by using a long short-term memory (LSTM) neural network. The use of transfer learning further demonstrates the LSTM model's remarkable generalization ability.

Description: Recent studies have been sparking concerns about the impending arrival of “tipping points” later in the 21st century. This study analyzes observed global surface temperature trends in three target latitudinal regions: the Arctic Circle, Tropics, and the Antarctic Circle. We show that global warming is accelerating unevenly across the planet, with the Arctic warming at more than three times the average rate of our world. We also analyzed the reliability of latitude-dependent surface temperature simulations from a suite of Coupled Model Intercomparison Project Phase 6 (CMIP6) models and their multi-model mean (MMM) by comparing their outputs to observational data sets. We selected the best-performing models based on their statistical abilities to reproduce historical, latitude-dependent values adapted from these data sets. The surface temperature projections were calculated from ensemble simulations of the Shared Socioeconomic Pathway 2–4.5 (SSP2–4.5) by the selected CMIP6 models. We estimate the calendar years of when surface temperatures will increase by 1.5, 2.0, and 2.5°C relative to the preindustrial period, both globally and in the three target regions. Our results reaffirm a dramatic, upward trend in projected surface temperatures, with unprecedented acceleration in the Arctic Circle, which could lead to catastrophic consequences across the Earth. Further studies are necessary to determine the most efficient solutions to reduce global warming acceleration and maintain a low SSP, both globally and regionally.

Description: Automatic earthquake monitoring is widely used by a seismic network and fast reported for significant events. Traditional earthquake location methods require P-wave and S-wave manual phase picking or accurate automatically obtained first arrivals as first step. The event location is determined based on the inversion to best fit of all travel times. In this study, we design a procedure to locate earthquakes based on back-projection stacking of seismic energy. The stacking of seismograms using a varying time delay computation based on a regional crustal model. The grid search is used to fine optimum location in 3-D spatial grids based on the maximal value of all stacked seismograms after tested all grids of the model. Synthetic test indicated the stable results to find collect location and original time.Taiwan is one of the most seismically active regions in the world. The seismic networks of CWBSN, TSMIP and BATS are designed for different purposes to record seismic ground motion in Taiwan. Almost those real-time data streams are continuously transmitted back to Academia Sinica. We have tested event location based the catalog of the CWB fast report using this dataset. The real data testing got high consistent results of event within network and moderate consistency for event located in the border of network. Our determination procedure is under polishing to get stable results and speed the process time. It provides a potential application to join the system of earthquake warning system in Taiwan and other seismic tectonic applications in the near further.

Description: An extreme drought occurred over Southeast China (SEC) in August 2019. We demonstrate synergistic effects of mid-latitude and tropical circulation on this extreme event and highlight the impacts of the coupling and locking of two cyclones at different latitudes, which are otherwise ignored. We propose the relaying roles of the Tibetan Plateau (TP) and western North Pacific in connection with the tropical convection and SEC precipitation. The equivalent-barotropic anticyclone over the TP and low-tropospheric cyclone over the western North Pacific both resulted from the positive Indian Ocean dipole and El Niño Modoki. The equivalent-barotropic cyclone over Northeast China originated from the dispersion of Rossby waves upstream along the subtropical waveguide associated with the North Atlantic tripole sea surface temperature anomaly pattern and the Rossby wave response to the TP precipitation deficiency. Further, they jointly contributed to this drought by inducing strong northerly wind anomalies in the entire troposphere over East China. These anomalous northerly winds led to decreased warm moisture from the south and substantial sinking motions, which inhibited the occurrence of the SEC local convection and precipitation. The SEC precipitation is closely related to convection over the Maritime Continent from a climate perspective. This relationship is verified by observations, linear baroclinic model experiments, and general circulation model sensitivity experiments with and without the TP, in which precipitation anomalies over the southern TP and Philippine Sea play important bridge roles. The results will advance the prediction of the SEC extreme drought events.

Description: A severe agricultural drought swept Central Asia in 2021, causing mass die-offs of crops and livestock. The anthropogenic contribution to declines in soil moisture in this region over recent decades has remained unclear. Here we show from analysis of large ensemble simulations that the aggravation of agricultural droughts over southern Central Asia since 1992 can be attributed to both anthropogenic forcing and internal variability associated with the Interdecadal Pacific Oscillation (IPO). Although the negative-to-positive phase transition of IPO before 1992 offset human-induced soil moisture decline, we find that the positive-to-negative phase transition thereafter has doubled the externally forced rate of drying in the early growing season. Human-induced soil moisture loss will probably be further aggravated in the following century due to warming, albeit with increasing precipitation, and our simulations project that this trend will not be counterbalanced by the IPO phase change. Instead, this internal variability could modulate drying rates in the near term with an amplitude of −2 (+2) standard deviation of the IPO trend projected to amplify (weaken) the externally forced decrease in surface soil moisture by nearly 75% (60%). The findings highlight the need for the interplay between anthropogenic forcing and the natural variability of the IPO to be considered by policymakers in this climate-sensitive region.

Description: Oil productions can result in pore pressure drop in the reservoir, generating an increase in effective stress and leading to reservoir compaction. The compaction in the subsurface reservoir translates to the earth's surface, which is manifested as a loss of elevation (land subsidence), causing damages to oil production facilities and surface infrastructures. The Liaohe oilfield, located in Liaohe River Delta (LRD), northeast of China, is one of the most significant subsidence areas in China as a direct consequence of oil extraction from the reservoir. Previous studies carried out in this area assumed the oil production-induced displacement retrieved from Interferometric Synthetic Aperture Radar (InSAR) corresponds only to vertical deformation. In this work, for the first time, we proposed a method to retrieve the full three-dimensional (3D) displacement field over the oilfield. We retrieved the vertical and east-west displacement components by combining the multiple-geometry InSAR line-of-sight (LOS) observations and retrieved the north-south component based on the assumption of a physical relationship between the horizontal and vertical displacement. Two ascending and two descending datasets from Sentinel-1 satellite covering the area were processed by an InSAR time series analysis over the 2017 to 2021 period, providing consistent displacement rate maps and displacement time series in the LOS direction. Spatial local-scale land subsidence was found in several producing fields over the deltaic region, including Shuguang, Huanxiling, and Jinzhou oilfields. The 3D displacement decomposition was then conducted in Shuguang oilfield. The derived 3D displacement field exhibit a circular subsidence bowl with a maximum subsiding rate reaching 212 mm/year, accompanied by a centripetal pattern of horizontal displacements with maximum rates up to 50–60 mm/year moving towards the subsidence center. The retrieved 3D displacements are in good agreement with predictions from the geomechanical modeling by assuming a disk-shaped reservoir subject to a uniform reduction in pore fluid pressure. Finally, we show the importance of knowing both the vertical and horizontal displacement in characterizing the lateral boundary of the subsurface reservoir.