Drought is a natural disaster that mainly affects water resources, agriculture, and a country’s social and economic development due to its long-term and frequent occurrence. Therefore, it is important to characterize and monitor drought and its propagation to minimize the impact. Precipitation and Terrestrial water storage are popular hydrological components used frequently to understand the drought process. However, spatiotemporal assessment of drought propagation over India based on terrestrial water storage is unexplored. In this study, the terrestrial water storage anomalies (TWSA) obtained from a Gravity Recovery and Climate Experiment (GRACE) and Combined Climatological Deviation Index (CCDI) are used to characterize the drought at different spatial scales. The changing pattern of TWSA and CCDI is investigated using the non-parametric Mann-Kendall test and its slope is estimated using the Theil-Sen slope estimator. TWSA trends unravel that the Ganga and Indus River Basin have significant negative trends throughout the year. However, the trends are oscillating for Peninsular basins. The major river basins Ganga, Indus, Brahmaputra shows decreasing trends for all seasons; however, Cauvery, Mahanadi, Krishna, and the Godavari show decreasing trends for winter and Post-monsoon season and increasing trends for pre-monsoon and monsoon season. It shows that Ganga, Indus, and Brahmaputra river basins are more prone to drought in the near future.

In an active volcanic arc, magmatically sourced fluids are channeled through the brittle crust by structural features. This interaction is observed in the Andean volcanic mountain belt, where volcanoes, geothermal springs and the locations of major mineral deposits coincide with NNE-striking, convergent margin-parallel faults and margin-oblique, NW/SE-striking Andean Transverse Faults (ATF). The Tinguiririca and Planchón-Peteroa volcanoes in the Andean Southern Volcanic Zone (SVZ) demonstrate this relationship, as both volcanic complexes and their spatially associated thermal springs show strike alignment to the outcropping NNE oriented El Fierro Thrust Fault System. This study aims to constrain the 3D architecture of this fault system and its interaction with volcanically sourced hydrothermal fluids from a combined magnetotelluric (MT) and seismicity survey. The 3D conductivity model and seismic hypocenter locations show correlations between strong conductivity contrasts and seismic clusters in the top 10km of the crust. This includes a distinct WNW-striking seismogenic feature which has characteristics of the ATF domains. As the surveyed region is characterized by high heat flow regimes, volcanic activity and hydrothermal systems related to the volcanic arc, the conductivity contrast suggests that magmatically derived fluids meet an impenetrable barrier, most likely the sealed core of the fault. The resulting increase in hydrostatic fluid pressure facilitates seismic activity on this WNW oriented structure. These results provides the first observation of the mechanism behind the reactivation and seismogenesis of ATF. The study also uncovers the role of the ATF the compartmentalization of magmatic-derived fluids that accumulate to form hydrothermal reservoirs in the SVZ.

The Geology teaching and learning processes should be carried out through the interaction of different learning environments, inside the classroom and outdoors. Practical activities are a methodological strategy widely suggested by the Portuguese Ministry of Education for the Geology teaching whose aim is to promote the development of skills (knowledge, ability and attitudes) denying the idea that the subjects of this science are complex and far from the students’ academic interests and needs. This work presents some formal and non-formal education practical activities (e.g. laboratory and fieldwork, mineral fairs, national contests), developed throughout the last year in Sé School, a small school in the north of Portugal, with students of the different grades (Kindergarten to high school).

Understanding catchment controls on catchment solute export is a prerequisite for water quality management. StorAge Selection (SAS) functions encapsulate essential information about catchment functioning in terms of discharge selection preference and solute export dynamics. However, they lack information on the spatial origin of solutes when applied at the catchment scale, thereby limiting our understanding of the internal (subcatchment) functioning. Here, we parameterized SAS functions in a spatially explicit way to understand the internal catchment responses and transport dynamics of reactive dissolved nitrate (N-NO3). The model was applied in a nested mesoscale catchment (457 km²), consisting of a mountainous partly forested, partly agricultural subcatchment, a middle-reach forested subcatchment, and a lowland agricultural subcatchment. The model captured flow and nitrate concentration dynamics not only at the catchment outlet but also at internal gauging stations. Results reveal disparate subsurface mixing dynamics and nitrate export among headwater and lowland subcatchments. The headwater subcatchment has high seasonal variation in subsurface mixing schemes and younger water in discharge, while the lowland subcatchment has less pronounced seasonality in subsurface mixing and much older water in discharge. Consequently, nitrate concentration in discharge from the headwater subcatchment shows strong seasonality, whereas that from the lowland subcatchment is stable in time. The temporally varying responses of headwater and lowland subcatchments alternates the dominant contribution to nitrate export in high and low-flow periods between subcatchments. Overall, our results demonstrate that the spatially explicit SAS modeling provides useful information about internal catchment functioning, helping to develop or evaluate spatial management practices.

We present detailed 3-D images of whole mantle P-wave velocity structure beneath Southeast Asia and surrounding regions. The results are obtained by applying an updated global tomographic method to invert ~8 million P, pP, PP, PcP, and Pdiff arrival times from 23,587 earthquakes recorded at 14,136 stations distributed all over the world. Our tomographic model reveals a continuous, thin low-velocity (low-V) zone from the surface to the core-mantle boundary beneath the Hainan hotspot, which may reflect the Hainan plume that exists in the whole mantle. Beneath the Australian slab that has subducted into the lower mantle, a strong low-V anomaly is detected, which may reflect subslab hot mantle upwelling (SHMU) due to return flow of the slab subduction. Our model also shows the distinct shape of subducted slabs in the upper mantle and slab remnants in the lower mantle. In particular, a hole in the subducting Australian slab is revealed at depths of 280–430 km beneath eastern Java. The low-V anomaly in the mantle wedge above the Australian slab is connected with the SHMU through the slab hole, suggesting that mixture of the island arc magma and the SHMU may have caused huge eruptions of the Tambora and Rinjani volcanoes in eastern Java.

We perform a suite of laboratory friction experiments on saw-cut Westerly Granite surfaces and probe frictional state evolution in response to step changes in normal stress. The experiments are conducted with the objective of illuminating the origin of friction memory effects and the fundamental processes that yield friction rate and state dependence. In contrast to previous works, we measure directly the fault slip rate and account for changes in slip rate caused by normal stress perturbations. Further, we complement mechanical data acquisition by continuously probing the faults with ultrasonic pulses. We conduct the experiments at room temperature and humidity conditions in a servo controlled biaxial testing apparatus in the double direct shear configuration. The normal stress perturbations are carried out during steady shearing over a range of shear velocities, from 0.02 - 100 μm/s. We report observations of a transient shear stress and friction evolution with step increases and decreases in normal stress. Specifically, we show that shear stress evolves in a two-stage fashion – first linear-elastically, then inelastically in response to the normal stress step. We find that the excursions in slip rate resulting from the changes in normal stress must be accounted for in order to accurately predict fault strength evolution. The effects of induced changes in fault slip rate are also apparent in elastic wave properties. Ultrasonic wave amplitudes increase instantly in response to normal stress steps and then gradually decrease to a new steady state value, in part due to changes in fault slip rate. This decrease is strongly related to accelerated creep at the fault interface. We also demonstrate that steady state amplitudes are a reliable proxy for real contact area (RCA) at the fault interface. Previous descriptions of frictional state evolution during normal stress perturbations have not adequately accounted for large slip velocity excursions. Here, we do so by using the measured ultrasonic amplitudes as a proxy for frictional state during transient shear stress evolution. Our work improves understanding of induced seismicity and triggered earthquakes with particular focus on simulating static triggering and stress transfer phenomena using rate-and-state frictional formulations in earthquake rupture models.

Bays within eastern boundary upwelling systems (EBUS) are ecological hot-spots featuring a diverse range of spatio-temporal dynamics. At the EBUSs’ poleward limit, upwelling occurs in short-lived (<1 week) pulses modulated by synoptic wind variability. The circulations in long, narrow bays can respond to these fluctuations within few hours. The short-term biological response to these pulses was investigated in two of these bays (Rias Baixas, NW-Iberia) with a two-week quasi-synoptic spatio-temporal survey in the summer 2018. A four-day-long upwelling pulse caused deep, nutrient-rich isopycnals to rise into the euphotic zone inside the bays, triggering a rapid (~1.7 days) nutrient uptake and formation of a subsurface chlorophyll maximum (~3.8 days). The phytoplankton biomass was transported rapidly toward deep, offshore waters when the winds weakened. These results suggest that high productivity in narrow bays is controlled by the transient exposure of deep, nutrient-rich waters to light during upwelling pulses.

Carbon-capture and sequestration (CCS) in geologic reservoirs is one strategy for reducing anthropogenic CO2 emissions from large-scale point-source emitters. Recent developments have shown that basalt reservoirs are highly effective for permanent mineral trapping on the basis of CO2-water-rock interactions, which result in the formation of carbonate minerals. However, the injection of super-critical CO2 into the subsurface causes a disturbance in the pressure, temperature, and chemical systems within the target reservoir. How the ambient conditions change in response to a CO2 injection ultimately affects the transport and fate of the injected CO2. Understanding the behavior and transport of CO2 within a geologic reservoir is a difficult problem that is only exacerbated by heterogeneities within the reservoir, for example, permeability can be highly heterogeneous and exhibits significant control on the movement of CO2. The non-isothermal effects that accompany a CO2 injection are also affected by heterogeneous reservoir properties. These non-isothermal effects can lead to significant changes in temperature in both the reservoir water and rock, which can also affect the injectivity and movement of the CO2. This study is focused on gaining a better understanding of the non-isothermal effects of a CO2 injection and how the changes in temperature will affect the movement and reactivity of the CO2, as well as investigate the efficacy of using temperature as a proxy for CO2 breakthrough in the subsurface. iTOUGH2 eco2n is utilized to simulate CO2 injections into a 1D radially symmetric domain over a range of permeabilities, porosities, and salinities. Preliminary results indicate that for a modest CO2 injection rate of 31,500 metric tons/year the exothermic dissolution of CO2 can cause a temperature increase of up to 3.6oC at the edges of the CO2 plume and the Joule-Thomson effect can cause a decrease in temperature up to 10oC near the injection well. Additionally, results from this study indicate that the thermal effects of a CO2 injection, namely the heat of dissolution associated with a large-scale CO2 injection may be an effective MMV strategy for monitoring CO2 breakthrough.

Analog methods (AMs) have long been used for precipitation prediction and climate studies. However, they rely on manual selections of parameters, such as predictor variables and analogy criteria. Previous work showed the potential of genetic algorithms (GAs) to optimize most of the AM parameters. This research goes one step further and investigates the potential of GAs for automating the selection of the input variables and the analogy criteria (distance metric between two data fields) in AMs. Our study focuses on the prediction of daily precipitation in central Europe, specifically Switzerland, as a representative case.  Comparative analysis against established methods demonstrates the superiority of GA-optimized AMs in terms of predictive accuracy. The selected input variables exhibit strong associations with key meteorological processes that influence the generation of precipitation. Further, we identify a new analogy criterion inspired by the Teweles-Wobus criterion, which consistently performs better than other Euclidean distances and could be used in classic AMs. In contrast to conventional stepwise selection approaches, GA-optimized AMs display a preference for a flatter structure characterized by a single level of analogy and an increased number of variables.Overall, our study demonstrates the successful application of GAs in automating input variable selection for AMs, with potential implications for application in diverse locations and data exploration to predict alternative predictands. In a broader context, GAs could be used to perform input variable selection in other data-driven methods, opening perspectives for a broad range of applications.

In recent years it has been recognised that parts of slow spreading ridges such as the Mid-Atlantic Ridge (MAR) are characterised by typical magmatic spreading, while other parts are characterised by the formation of detachment faults and Oceanic Core Complexes (OCC). These different spreading modes can be clearly identified in the near-ridge environment in the bathymetry, with magmatic mode crust characterised by linear fault-bounded ridges, and detachment mode crust by more chaotic bathymetric signatures. The aim of this project is to characterise the magnetic and gravity signatures of lithosphere created by different modes of spreading, with the aim of using these signatures to identify if the structures still remain in ocean-continent transitions, where they have been covered by sediments coming from the continental crust. We first characterise different modes of spreading using available high-resolution bathymetry data of the MAR up to 20 My of age. The identified characteristics are then related to the corresponding ship-borne gravity and magnetic data in the same area. From the gravity anomalies, thinner crust is observed where the OCCs are in place. This allows the mantle to be exhumed to the sea-floor. As for the magnetic anomalies, it is found that in places where OCCs are present, the anomalies are not as symmetrical as those found in magmatic mode regions. We present a range of parameters extracted from the data that characterise different spreading modes, and use these to test whether transitions between detachment and magmatic mode crust identified in the bathymetry can be readily identified in gravity and magnetic data.

Accessibility of scientific research to underrepresented voices is a forefront issue. Intentionally-designed citizen science programs, such as the University of Alaska Fairbanks’ (UAF) citizen science and learning research project “Arctic Harvest-Public Participation in Scientific Research,” are poised to improve the participation and effectiveness of citizen science across diverse audiences while gathering rigorous data. We selected our research question after first listening to stories of how Alaska’s rural grocery store - the land and its bounty of berries - has become more variable. We investigate how shifts in climate affect the fate of subsistence berries and timing of berry loss from plants in fall and winter across Alaska. In our presentation, we outline the design elements and accommodations we made to enable a diverse group of 1,099 participants in 28 communities to collect phenology, berry abundance and condition, temperature, cloud cover, and snowpack data across ages, cultures, and learning environments. Over half of our volunteers were pre-K to 6th grade, while just under 10% were adults. Approximately 44% of our participants (479 of 1099 participants) were from groups historically underrepresented in STEM fields. We present learning outcomes evaluation, data collection approaches, and data quality per age group. To improve the success of a citizen science program, find the overlap between a topic of personal and cultural relevance to diverse participants and your university’s ongoing research programs.

This study presents a workflow to predict the upscaled absolute permeability of the rock core direct from CT images whose resolution are not sufficient to allow direct pore-scale permeability computation. This workflow exploits the deep learning technique with the data of raw CT images of rocks and their corresponding permeability value obtained by performing flow simulation on high resolution CT images. The permeability map of a much larger region in the rock core is predicted by the trained neural network. Finally, the upscaled permeability of the entire rock core is calculated by the Darcy flow solver, and the results showed a good agreement with the experiment data. This proposed deep-learning based upscaling method allows estimating the permeability of large-scale core samples while preserving the effects of fine-scale pore structure variations due to the local heterogeneity.

Moderate earthquakes (Mw > 5) with moment tensors (MTs) dominated by a vertical compensated-linear-vector-dipole (vertical-CLVD) component are often generated by dip slip along a curved ring-fault system at active volcanoes. However, relating their MTs to ring-fault parameters has been proved difficult. The objective of this study is to find a robust way of estimating some ring-fault parameters based on their MT solutions obtained from long-period seismic records. We first model the MTs of idealized ring-faulting and show that MT components representing the vertical-CLVD and vertical strike-slip mechanisms are resolvable by the deviatoric MT inversion using long-period seismic waves, whereas a component representing the vertical dip-slip mechanism is indeterminate owing to a shallow source depth. We then propose a new method for estimating the arc angle and orientation of ring-faulting using the two resolvable MT components. For validation, we study a vertical-CLVD earthquake that occurred during the 2005 volcanic activity at the Sierra Negra caldera, Galápagos Islands. The resolvable MT components are stably determined with long-period seismic waves, and our estimation of the ring-fault parameters is consistent with the ring-fault geometry identified by previous geodetic studies and field surveys. We also estimate ring-fault parameters of two earthquakes that took place during the 2018 activity at the caldera, revealing significant differences between the two earthquakes in terms of slip direction and location. These results show the usefulness of our method for estimating ring-fault parameters, enabling us to examine the kinematics and structures below active volcanoes with ring faults that are distributed globally.

A document by Liangjin Song. Click on the document to view its contents.

We present a book entitled “Machine Learning, Statistics, and Data Mining for Heliophysics,” an online and open source book available at helioml.org. This book includes a collection of interactive Jupyter notebooks, written in the programming language Python, that walks the reader through the process of applying machine learning, statistics, and data mining techniques on various kinds of solar and space physics data sets to reproduce published results. We consider this book to be a living document with frequent updates. Please contact us if you’d like to submit a chapter!

Our understanding of tree stem methane (CH4) emissions is evolving rapidly. Few studies have combined seasonal measurements of soil, water and tree stem CH4 emissions from forested wetlands, inhibiting our capacity to constrain the tree stem CH4 flux contribution to total wetland CH4 flux. Here we present annual data from a subtropical freshwater Melaleuca quinquenervia wetland forest, spanning an elevational topo-gradient (Lower, Transitional and Upper zones). Eight field-campaigns captured an annual hydrological flood-dry-flood cycle, measuring stem fluxes on 30 trees, from four stem heights, and up to 30 adjacent soil or water CH4 fluxes per campaign. Tree stem CH4 fluxes ranged several orders of magnitude between hydrological seasons and topo-gradient zones, spanning from small CH4 uptake to ~203 mmol m-2 d-1. Soil CH4 fluxes were similarly dynamic and shifted from maximal CH4 emission (saturated soil) to uptake (dry soil). In Lower and Transitional zones respectively, tree stem CH4 contribution to the net ecosystem flux was greatest during flooded conditions (49.9 and 70.2 %) but less important during dry periods (3.1 and 28.2 %). Minor tree stem emissions from the Upper elevation zone still offset the Upper zone CH4 soil sink capacity by ~51% during dry conditions. Water table height was the strongest driver of tree stem CH4 fluxes, however tree emissions peaked once the soil was inundated and did not increase with further water depth. This study highlights the importance of quantifying the wetland tree stem CH4 emissions pathway as an important and seasonally oscillating component of wetland CH4 budgets.

Better understanding of the hydrological cycle of the Lower Mekong River Basin (LMRB) and addressing the value-added information of using remote sensing data on the spatial variability of soil moisture over the Mekong Basin is the objective of this work. In this work, we present the development and assessment of the LMRB (drainage area of 495,000 km2) Soil and Water Assessment Tool (SWAT). The coupled model framework presented is part of SERVIR, a joint capacity building venture between NASA and the U.S. Agency for International Development, providing state-of-the-art, satellite-based earth monitoring, imaging and mapping data, geospatial information, predictive models, and science applications to improve environmental decision-making among multiple developing nations. The developed LMRB SWAT model enables the integration of satellite-based daily gridded precipitation, air temperature, digital elevation model, soil texture, and land cover and land use data to drive SWAT model simulations over the Lower Mekong River Basin. The LMRB SWAT model driven by remote sensing climate data was calibrated and verified with observed runoff data at the watershed outlet as well as at multiple sites along the main river course. Another LMRB SWAT model set driven by in-situ climate observations was also calibrated and verified to streamflow data. Simulated soil moisture estimates from the two models were then examined and compared to a downscaled Soil Moisture Active Passive Sensor (SMAP) 36 km radiometer products. Results from this work present a framework for improving SWAT performance by utilizing a downscaled SMAP soil moisture products used for model calibration and validation.

Recent advances in imaging techniques and related modeling have made it possible to study micron and sub-micron scales in unprecedented detail. Currently, performing direct simulations at a representative scale has proven computationally prohibitive, hence, the ability to simulate flow cannot keep up with the size of images available (e.g. x-ray micro-tomography or large area scanning electron microscopy). To overcome this issue, we propose to train a neural network architecture to understand relationships between pore-scale morphology and the simulation outputs. With this, we improve our portability and prediction time. Convolutional neural networks are attractive for this task because they support flexible input size, they are able to capture local interactions, and they can find places that present similar patterns. Generally, deeper networks have better prediction performance, but they are very difficult to train due to the vanishing gradient problem. Also, information from different scales is commonly lost along the network.