For many years, rainfall has been measured in Brazil on a daily basis only; hence it is unusual to find studies considering a better temporal resolution for the whole country. However, since 2013, the National Centre for Monitoring and Early Warning of Natural Disasters (CEMADEN) has started to monitor rainfall in a sub hourly basis at more than 3,000 gauges. This study is the first to analyze sub-daily characteristics of rainfall in Brazil on national scale. Raw data from 3 years (2015-2017) was downloaded from CEMADEN’s home page and then quality control procedures were applied to select the high-quality rain gauges. Then, two rainfall properties were calculated: (i) Number of Wet Days – NWD, which is the number of rainy days per year; and (ii) Number of Effective Wet Days – NEWD, which is the number of rainy hours per year divided by 24 hours. Both NWD and NEWD were grouped according to NEWD values and spatially analyzed. About 1,100 rain gauges were used in this study: 824 (2015), 1,288 (2016), and 1,385 (2017). NWD ranges from 26 to 226 days (average: 151 days), while NEWD ranges from 1.2 to 47.8 days (average: 14.3 days). Results showed that group A (NEWD < 10.3 days) contains gauges located in the Brazilian Mid-western and northeastern semiarid regions. Group B (10.3 ≤ NEWD < 13.2 days) occurs in parts of the coastal region of Northeast and highlands of Southeast. Groups C (13.2 ≤ NEWD < 17.4 days) and D (NEWD ≥17.45 days) are found in South and part of the Southeastern coastal region. The region with the most concentrated rainfall rates (group A) is the one with the lowest annual rainfall. Orographic effects seem to cause reduction in NEWD, from 15.2 days to 11.8 days (group B). Groups C and D comprehend the rainiest region, preceded by the Amazon region. Finally, we highlight that spatial distribution of NWD and NEWD did not change abruptly annually, with an overall ratio between them of ~10 times. Although in some regions rainfall occurs in four months, it effectively falls from 10 to 13 days. This study is important to understand how concentrated the rainfall is in the different Brazilian regions.

As a result of climate change, California is experiencing the impact of more extreme weather patterns including longer drought periods and atmospheric rivers resulting in extreme snow pack and heavy flood flows. CA faces a significant challenge to mitigate these impacts while simultaneously providing resilient sources of water under uncertain future conditions. One approach that addresses both flood mitigation and water storage is the use of Managed Aquifer Recharge (MAR). Ventura County Waterworks District #1 (VCWWD) is designing a MAR recharge facility to divert flood flows in the adjacent Arroyo Las Posas to a series of engineered basins, where water will infiltrate and replenish the local aquifer (estimated recharge: 3000 acre-feet annually). However, large uncertainties in percolation rates and an inability to predict or improve percolation (measured: 5 and 16 cm/day) places large uncertainties on the facility’s ultimate performance (and impact) on VCWWD’s overall strategy for sustainable groundwater management. The goals of this project are to use a suite of geophysical techniques, point sensors and novel modeling approaches to measure the basin(s) spatial recharge rates, where and how the water is infiltrating (fast paths) and how will basin modification improve recharge rates. Selected basins will first be characterized using electromagnetic methods and electrical resistivity tomography (ERT) coupled with soil cores to estimate the distribution of subsurface permeability in order to design the infiltration monitoring layout. During managed flooding events Spontaneous Potential will be used to monitor subsurface leakage from the basins back into the river. Within a basin, novel vertical Distributed Temperature Profiling sensors will measure diurnal temperature fluxes to calculate spatially distributed 1-D vertical recharge rates and 3D time-lapse ERT to monitor and measure the spatially dynamic recharge. ERT results will be coupled with multi-point geostatistical simulations to estimate soil permeability field scenarios and with novel joint inversion codes to estimate volumetric recharge and rates, offering a powerful suite of tools for water managers to quantify, and potentially improve basin recharge rates and develop operational and maintenance plans to maximize recharge.

We explore the possibility that Callisto’s ocean sits beneath its high-pressure ice, rather than above it. Oceans perched between ice phases are considered to be stable configurations for Ganymede, Callisto, and Titan. High-pressure ices under the liquid water ocean will transport heat and solutes into the ocean as long as the convective adiabat for the ices remains close to the melting temperature (Choblet et al. 2017, Kalousova and Sotin 2018). However, this configuration may become unstable when the perched ocean is close to freezing and its salinity increases, if the ocean becomes denser than the underlying ice. Among the oceans in the solar system, Callisto’s must be among the coldest and most saline because the internal heat appears to be low in the absence of tidal dissipation. Surface geology indicates its lithosphere is fully stagnant (Moore et al. 2004). Solid-state convection may continue beneath less than 100 km or dirty non-convecting ice (McKinnon 2006). And just below this layer may reside a liquid water ocean that is the lag deposit of Callisto’s thicker primordial ocean, the concentrated result of 4 Gyr of freezing. Using representative interior structures based on the current constraints from the Galileo mission (Anderson et al. 2001) coupled with recently obtained thermodynamic data (Vance et al. 2018), we demonstrate the possibility for using magnetic induction to identify where the ocean currently resides in Callisto. Anderson, J. D. et al. (2001). Shape, mean radius, gravity field, and interior structure of Callisto. Icarus, 153(1):157–161. Choblet, G. et al. (2017). Heat transport in the high-pressure ice mantle of large icy moons. Icarus, 285:252–262. Kalousovà, K. and Sotin, C. (2018). Melting in high-pressure ice layers of large ocean worlds - implications for volatiles transport. Geophysical Research Letters. McKinnon, W. (2006). On convection in ice I shells of outer solar system bodies, with detailed application to Callisto. Icarus, 183(2):435–450. Moore, et al. (2004). Callisto. Jupiter. The Planet, Satellites and Magnetosphere, 1:397–426. Moore, J. and Pappalardo, R. (2011). Titan: An exogenic world? Icarus, 212:790–806. Vance, S. D. et al. (2018). Geophysical investigations of habitability in ice-covered ocean worlds. Journal of Geophysical Research: Planets, 123, 180–205.

The Khumbu Icefall lies at an approximate altitude of 5,500 meters on the Nepali side of Mt. Everest and is one of the most dangerous stages of the major South Col route to the summit. In the Khumbu Icefall, seracs, or ridges on the surface of a glacier, are known to collapse unexpectedly, displacing massive volumes of ice down the Khumbu Glacier, estimated to advance 0.9 to 1.2 m down the mountain every day. Satellite Synthetic Aperture Radar (SAR) uniquely enables a next generation of remote sensing capabilities that offer a persistent, all-weather, day/night, complete characterization of the Earth and its dynamic surface changes. Using open C-band SAR data from the Sentinel-1 satellite constellation, ice dynamics can be reliably tracked at an approximately bi-monthly cadence over the past five years. The C-band instrument is nearly unaffected by cloud cover as it is not significantly attenuated by water vapor, making it incredibly useful for time-sensitive and temporal analysis such as tracking ice volumes, especially in regions under heavy cloud cover for roughly half of the year. Also, the Radar signal can be unwrapped to create phase history data to unlock information about the movement of objects in the scene. We demonstrate the use of this complex data to perform very accurate measurements of 3D change across the glacier and to extract two dimensional (azimuth and range directions) glacier surface velocities through the use of SAR speckle tracking. By enhancing 3D static geospatial data in the form of 3D high-resolution surface models with dynamic SAR information in 4D (3D + time) we additionally demonstrate a unique approach for more effective multi-temporal analysis and 3D change detection. We fuse the enhanced SAR open data with commercial 3D surface models to build a temporal stack of imagery over the Khumbu region and track changes in volume over time and their corresponding velocities. We showcase our analysis using 3D renderings of SAR fused data and SAR derived analytics for enhanced interpretability of the results. This data-driven time series coupled with geophysics principles can lead to significant research advances in studying glacier loss or retreat, ice thinning, or forecasting probabilistic ice motion to better prepare expeditions.

The growth of biofilms changes the hydrodynamics of porous media, which will influence the transport of bacteria and contaminants in natural and engineered systems. Traditionally, biofilms have been modeled as an impermeable domain in porous media, such that no water can enter biofilms and contaminants can only enter biofilms via molecular diffusion. Such a modeling approach is based on the assumption that the permeability of biofilms is the same as that of Extracellular Polymeric Substances (EPS), which are considered to have very low permeability. In this study, we investigate the impacts of biofilm properties on water flow using microfluidic device experiments and pore-scale modeling. E. coli biofilm was established inside a microfluidic channel packed with unisized glass beads in a single layer. A 5*5 mm2 area was live-stained and imaged via confocal microscopy at three different growth stages to represent three biofilm levels in the system. After image analysis using FIJI and AutoCAD software, the flow in the bio-clogged porous media was simulated using COMSOL Multiphysics. In these simulations, biofilm was modeled as a separate permeable domain in porous media instead of an impermeable domain. A Forchheimer-corrected version of the Brinkman equations was applied to simulate the flow in the porous biofilm regions. Two properties of biofilms, namely biofilm porosity (BPO) and biofilm permeability (BPE), were altered to examine their effects on the permeability of the system. It was found that different values of BPO and BPE clearly affect the flow paths, velocity patterns, and permeability of the system. Considering biofilms as impermeable results in significant underestimations of the flow properties. In addition, two simplified modeling scenarios, namely uniform coating and symmetric contact filling, were investigated for a possible abridging in the arduous modeling procedure of the real biofilm geometry.

In subduction zones, megathrust seismicity depends on the hydrogeological, physical and mechanical properties of sediments before they enter the subduction zone and how these properties evolve through the subduction process. In particular, fluids are progressively released by compaction and/or mineral dehydration reactions as burial increases, resulting in the build-up of pore fluid pressure in low-permeability sediments that strongly affects fault behavior through its control on effective normal stress. Thus, characterizing the compaction state and bound water content of sediments in subduction systems is crucial. During IODP Expedition 372 and 375, Site U1518 was drilled in the frontal wedge of the Northern Hikurangi margin ~6.5 km west of the deformation front. This site penetrated an active thrust fault, the Papaku fault, its hanging wall and uppermost footwall. The Papaku fault, intersected at ~304 meters below sea floor, is a westward-dipping splay fault which is thought to lie in the SSE rupture area, to host SSEs and to have accommodated several kilometers of shortening within the prism. It is composed of a ~18m-thick main fault zone with a mixture of brittle and ductile structures and ductile features locally overprinted by brittle fractures and faults. Below, there are a ~21m-thick zone of gradually decreasing brittle-ductile deformation and a ~10m-thick subsidiary fault zone. The folded, faulted and pervasively fractured hanging wall corresponds to Early-Mid Pleistocene hemipelagic silty-claystone with fine-grained turbidites sequences. The footwall is composed by relatively undeformed Mid-Late Pleistocene bioturbated hemipelagic mudstones with turbidites sequences. We use IODP expeditions 372-375 and post-cruise porosity, logging and chemical data to characterize the porosity of sediments at Site U1518, including interstitial porosity, bound water content and fracture porosity. Interstitial porosity is obtained by correcting total porosity measured onboard accounting for clay-mineral bound water using Cation Exchange Capacity (CEC). Unlike total porosity measured onboard, interstitial porosity is representative of the compaction state of sediments. To better characterize interstitial porosity, we document the evolution of the structure of meso- to macropores with depth using mercury injection capillary pressure and low-field nuclear magnetic resonance. We assess the compaction state of sediments at Site U1518 by comparing the interstitial porosity-effective vertical stress curve with that of equivalent siliciclastic units at Site U1520. This site sampled and logged the undeformed input sedimentary section and the top of the oceanic crust ~95 km from shore. We show that the hanging-wall of the Papaku thrust fault is overconsolidated whereas the footwall is normally consolidated. Finally, we discuss deformation history of sediments during accretion regarding the compaction profile and the deformation structures observed at Site U1518.

Changes in the heavy ion composition in the terrestrial ionosphere and magnetosphere can have significant impact on particle dynamics in the Earth’s magnetosphere-ionosphere system. Most instruments flying in space, such as MMS and Van Allen Probes, lack the possibility to distinguish N+ from O+ due to their close masses. However, observations of N+ both in the ionosphere and magnetosphere indicate that N+ is a constant companion of O+ , especially during the storm time. Because N+ originates from the Earth’s ionosphere, we further develop the Polar Wind Outflow Model (PWOM) to investigate the behavior and acceleration mechanisms of heavy ions in Earth’s ionosphere. The PWOM solves the particle dynamics of O+, H+ and He+ in the ionospheric outflow and the modified PWOM can further simulate the behavior of N+ and N2+ in Earth’s polar wind. The escape of heavy ions from the Earth atmosphere is consequences of energization and transport mechanisms, including photo ionization, electron precipitation, ion-electron-neutral chemistry and collisions. The modified PWOM is coupled with a two-stream model of superthermal electrons (GLobal airglow, or GLOW) to deal with attenuated radiation, electron beam energy dissipation, and secondary electron impact. In this study, we show that during various solar conditions, the ion-electron-neutral densities in the ionospheric outflow show significant difference when we consider N+ ions in the polar wind. Furthermore, we will compare the simulation results of the modified PWOM with observation data for validation.

Up-to-date information about vegetation types and hydromorphological structures and features are essential for the management of waterways. They are e.g. used for the monitoring and reporting of riparian statuses and their changes e.g. after river restoration and consequently, numerous man-days are spent on field surveys. To allow for an effective survey of vegetation and hydromorphology in large or even inaccessible areas, a data acquisition and processing workflow is being developed complementing in-situ methods with remote sensing techniques. This is part of the joint research project “mDRONES4rivers” funded by the German Federal Ministry of Transport and Digital Infrastructure (19F2054A). Aerial surveys by unmanned aerial systems (UAS) and a gyrocopter are combined with ground measurements of hyperspectral reflectance signatures as well as with field mapping of vegetation types and hydromorphological structures and features. The remote sensing data is classified with an object based image analysis and classification algorithm. The mobile and (at selected sites) permanent measurements of hyperspectral field data and the typical field surveys provide data for calibration. Contrary to other approaches that focus on what can be detected and classified with certain sensor systems and datasets, the project addresses equally the user needs to obtain certain classes for monitoring and reporting. The intended results are (i) data acquisition, correction and classification workflow combining remote sensing and field data, identification and change detection (ii) of important vegetation and biotope types and (iii) of hydromorphological structures and substrate as well as indicators necessary for the evaluation of the hydromorphological quality. The preliminary results to be presented include datasets from UAS, gyrocopter, and field surveys, an outline of processing workflow and classification algorithm based on Python scripts and eCognition software and first vegetation and hydromorphological classification results from spring and summer datasets. In conclusion, procedures and algorithms are developed to use remote sensing in combination with and for the reduction of time-consuming traditional field surveys as a future operational tool for monitoring riparian vegetation and structures.

Electromagnetic waves propagating through the Earth’s ionosphere are subjected to changes in group and phase velocities, refraction, dispersion, and diffraction. For systems like GPS, which relies on the usage of L-band signals, rapid and random fluctuations in the phase and amplitude (known as scintillation) of the signals passing through the ionosphere play a major role, as they may cause losses of lock and result in degrading the accuracy and reliability of such systems. Therefore, understanding the physical nature and ability to predict the scintillation has been a challenge since a long time for engineers and scientists. In this work, a climatological model of rapid random fluctuations in phase and amplitude of GPS signals has been presented for high latitudes of the northern hemisphere. The 50Hz GPS raw data from Canadian High Arctic Ionospheric Network (CHAIN) for the 24th solar cycle (2008-2019) have been used to study the climatology of the rapid fluctuations in phase and amplitude of the GPS signals. The statistical analysis has been performed in terms of phase and amplitude scintillation indices (𝜎𝜑 and 𝑆4). The results are presented for different geo- and helio-physical conditions, including solar and geomagnetic activity, season, local time and geographical/geomagnetic location of ionospheric pierce points. For the first time, the distribution of the signal phase and amplitude fluctuations are presented for the whole period of the 24th solar cycle. An important quantitative statistical relation of the phase and amplitude fluctuations in GPS signals have been established for the high latitude region. A theoretical explanation is suggested for the observed differences in phase and amplitude fluctuations.

At regional scales, electrical resistivity illuminates Earth processes involving fluid evolution and transport, temperature contrasts, and fault characteristics and behavior. It also clarifies continental terrane assembly and event sequencing through electronic mineral markers. Magnetotellurics (MT) is sensitive over such scales, but faces high property contrasts, small signals, 3-D complexity, discontinuous fields, and ill-posed inversion. So-called wideband (~0.003 – 500 s wave period) MT recording constrains crustal structure, and high fidelity through its central dead band is routinely achieved now via distant remote referencing, continuous streaming, and outlier removal. To resolve across the upper mantle, long period data must be of high quality through 10,000 s. Electronics modifications now permit good quality MT data over polar ice-covered regions, and non plane wave outliers appear largely avoidable. Regularized 3D non-linear inversion using simulation equations that recognize a spatially discontinuous electric field has become common practice and lends essential credibility to interpretations. However, resistivity model non-uniqueness is seldom tested enough, and assuming isotropic resistivity can lead to artifacts. Fluids interpreted to cause low resistivity in ductile deep crust should be at lithostatic pressures and have compositions compatible with ambient temperature and metamorphic grade. Vertical current channeling enhances resolution of large-scale fault zones connecting deep and shallow structures. Stabilized terranes can exhibit strong, quasi-linear conductors marking belts of graphite or sulfides deposited in sediment-starved foredeeps or rift margin basins, with a particular concentration in the Proterozoic corresponding to atmospheric oxygenation events. An exciting recent avenue is estimating H2O content of nominally anhydrous minerals (NAMs) in the upper mantle, which strongly affects electrical conductivity but not seismic velocity. The large bandwidth of MT data affords a broad-scale, unified view of Earth processes from mantle level sources through crustal storage and evolution to near-surface deposition. Support has been from U.S. Dept of Energy contract DE-0006732 and National Science Foundation grant OPP-1443532, and numerous prior.

Providing accurate forecasts of human modified streamflow is critical for applications ranging from natural resource management to hydropower generation. In this study we evaluate the performance of Long Short Term Memory (LSTM) based neural networks, which maintain an internal set of states and are therefore well-suited to modeling dynamical processes. This research builds on previous work demonstrating that an LSTM model can predict streamflow in out of sample basins with similar or greater accuracy than traditional forecast models specifically calibrated on those basins [1]. Using meteorological data from NOAA’s Global Forecasting System (GFS) and North American Land Data Assimilation System (NLDAS), remote sensing data including snow cover, vegetation and surface temperature from NASA’s MODIS sensors and streamflow data from USGS, we first train an LSTM model on 100 unmodified river basins, and evaluate its predictions on previously unseen human-altered basins. We then train models on a combination of natural and human modified basins and experiment with the effects of new data sources and additional model architecture in predicting human altered streamflow. By training on multiple basins with dynamic climate, land surface and human inputs, we can test the model’s understanding of general hydrologic relationships and human use patterns. We evaluate our models on “out of sample” rivers (previously unseen by the model) that have been altered by dam operations and agricultural withdrawals in northern California. We find that the models trained on natural and modified basins capture human modified flows better than our baseline model trained on natural basins. [1] Kratzert, F., Klotz D., Shalev, G., Klambauer, G., Hochreiter, S. & Nearing, G. Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling. Preprint at https://arxiv.org/abs/1907.08456 (2019)

Monsoon low pressure systems (LPS) are synoptic scale tropical disturbances that form over the Indian subcontinent along the quasi-stationary trough axis during the monsoon (June to September) period. Around 14 LPS form every year, accounting for around 44% of monsoon precipitation and 78% of extreme precipitation events over the country. Many past studies have investigated the influence of various topographical features on the Indian monsoon. This study investigates the influence of the Himalayan and Tibetan orography (HTO) on various LPS-related characteristics/features (genesis location, number, tracks, and intensity). The NCAR Community Earth System Model (CESM1.2.2) is used to study the influence of HTO on monsoon and LPS activity over India. Simulations from CESM1.2.2 are obtained at 0.9°×1.25° horizontal resolution by considering the present-day height (h) of HTO, and altered heights (zero, 0.5h, and 1.5h). A 9.3% increase in the average monsoon precipitation is simulated over India when the height of HTO is increased to 1.5h, while a decrease in the same by 11.5% (44%) is simulated when the height of HTO is reduced to 0.5h (zero). These results are consistent with previous modeling studies. The changes in monsoon precipitation are attributed to a strong (weak) mean meridional temperature gradient (MTG) associated with an increase (a decrease) in the height of HTO and the prevention of cold dry mid-latitude air mixing with the warm humid air over India. Furthermore, we find that the simulated number of LPS per year increases when the height of HTO is reduced. The number of LPS is 17.2, 16.1, 13.6, and 12.4, respectively, in the simulations where the height of HTO is zero, 0.5h, h, and 1.5h. The mean meridional width of the LPS active region also increases when the height of HTO is reduced (Fig. 1). Contrary to the expectation of a southward shift in the LPS median track with a decrease in MTG (when the height of HTO is reduced), a slight northward shift is simulated in the track’s location. We attribute this to an increase (a decrease) in barotropic instability on reducing (increasing) the height of HTO, which results in a larger latitudinal spread in the location of genesis and tracks. The increased (decreased) barotropic instability also causes an increase (a decrease) in the frequency of LPS over the Indian subcontinent.

Transportation fuels flow through a complex supply chain from the point of crude oil extraction to the point of combustion. We present a model that tracks the movement of gasoline and diesel across the petroleum infrastructure network consisting of pipeline, tankers, trucks, trains, refineries, and blenders. While direct CO2 emissions, from combustion, outweigh all supply chain emissions from processing and fuel movement, the indirect CO2 emissions also contribute a not insignificant amount of emissions driven by demand of transportation fuels. We resolve county-scale supply chain (Scope 3) CO2 emissions using publicly accessible data to quantify fuel movement between different linkages and transportation modes across the country. For most of the US, the exact volume of fuel moved between counties, from different refineries, along different modes of transportation, is not explicitly known. Linear optimization is used to model these flows with supply and demand related constraints. This work presents the most complete view of spatially-resolved scope-3 style CO2 emissions from United States’ road transportation fuels. It offers a chance to investigate spatial patterns of scope-3 emissions across the country, as well as spatial differences between scope-1 and scope-3. Understanding embodied CO2 emissions of commodity flows across the US has implications for national and local policy.

In recent decades, the intensity, frequency, and variability of extreme events (e.g., heat waves, droughts, severe storms, and floods) have increased globally due to the intensification of the changing climate, land-use/landcover and anthropogenic activities. An increase in extreme events results in severe economic crises and enormous disruptions in various social and environmental sectors. Hence, effective and efficient prediction and mitigation of flood and drought events are desirable. It requires a better understanding of the recent trends and variability of the extremes, which is quite challenging owing to the complex and non-stationary behaviour of the associated covariates (hydroclimatic variables, atmospheric circulation patterns), and their spatiotemporal variability and nonlinear interactions. This study proposes a new Non-stationary Standardized Potential Evapotranspiration Index (NSPEI) to assess the wetness and dryness condition and couples it with an entropy-based measure to quantify the variability of the extremes. NSPEI considers non-stationarities of both precipitation and temperature in addition to their ability to identify extreme events at different time/accumulation scales (multi-scalar). Hence, water deficits/excess could be better assessed over different accumulation periods, which helps identify and monitor various droughts (e.g., agricultural, meteorological) and water saturation conditions (e.g., floods, runoff). Furthermore, as the drought/wetness triggering variables and causes for extreme conditions may vary worldwide, a standardized drought variability index is introduced in this study to assess better drought and wetness variability across the world at multiple timescales (monthly, seasonal, annual and decadal). In addition, the influence of LULC and location indicators (latitude, longitude, and elevation) on drought and wetness variability is assessed across different continents for various time scales and severity of both wetness and dryness conditions. The analysis and outcomes of this study enhance understanding of global drought and wetness variability patterns and provide reliable information on water availability for devising effective water management strategies towards mitigation of the extremes in various continents.

Tropical convective organization and associated clusters of precipitation affect the global circulation and Earth’s energy budget, but many aspects such as the power-law distribution of precipitation clusters and the relation to convective self-aggregation remain poorly understood. Here, we present a physics-informed 2D conceptual model for tropical convective organization. The model is based on the budget equation of column moist static energy (CMSE) with terms that are parameterized based on diagnosing them in a high-resolution simulation with explicit convection. The conceptual model combines a reaction-diffusion equation, where the reaction term has memory, with a temporal red noise. We find that vertical advection has a strengthening effect on CMSE perturbations, whereas horizontal advection has a mitigating effect on CMSE perturbations. The CMSE tendencies from vertical and horizontal advection terms are larger in magnitude than those from radiation and surface fluxes. The strengthening effect of vertical advection corresponds to a negative gross moist stability (GMS) at short spatiotemporal scales, but the GMS becomes positive when the convection changes from shallow to deep, killing off the growth in CMSE. The conceptual model is faithful in simulating self-aggregation in that it shows a domain-size dependence of aggregation found in many prior works, and its CMSE power spectrum matches that of the high-resolution simulation except for a shallowing of the slope at high wavenumbers. Through analyzing the conceptual model equation, we show that 1) the domain-size dependence of self-aggregation is due to a competition between the strengthening effect of vertical advection and the smoothing effect of horizontal advection, and 2) the combination of temporal red noise and diffusive horizontal advection sets the shape of the CMSE power spectrum. Furthermore, the conceptual model also reproduces power-law distributions of precipitation clusters when precipitation clusters are viewed as thresholded islands on the CMSE topography. Thus, the simple conceptual model captures and helps to explain important aspects of convective self-aggregation and tropical convective organization.

Fractures play an important role in transport in unconventional formations. Both matrix and fracture network in fractured media contribute to the effective permeability (keff). In this study, we investigate the interaction between matrix and fracture permeabilities (km and kf) via the percolation-based effective-medium approximation (P-EMA). We theoretically determine the keff value at different fracture densities and compare our results with two- and three-dimensional numerical simulations. We found that the P-EMA estimated the effective permeability accurately in the matrix-fracture systems studied here. Further investigations are needed to better understand how the parameters of the P-EMA including scaling exponent and critical fracture density might vary from one fractured reservoir to another.

Filamentous fungi in soil branch hyphae through pores, creating an interconnected fiber network, which is known as mycelium. Fungal mycelium can cross-link and entangle soil particles, which alters soil pore structures. Fungi can also secret hydrophobic compounds, changing the water wettability of soils. These fungal traits can affect the hydraulic properties of soils. This study investigated the effect of fungi on soil water retention and hydraulic conductivity of the Ottawa 50/70 sand treated by a saprotrophic fungus, Trichoderma virens (commonly existing in soil). The soil water retention curve (SWRC) and hydraulic conductivity tests were performed on fungi-treated and untreated Ottawa 50/70 sand. Water repellency of fungi-treated sand was also assessed by measuring contact angles (between the water droplet and fungi on sand specimen) and water drop penetration time. The results of SWRC tests showed an approximate 6-fold increase of air entry suction in the fungi-treated sand, indicating the fungal treatment improved water retention capability. The increased air entry suction was attributed to the change of pore geometry due to mycelium network. A 2-fold reduction in hydraulic conductivity was observed in the fungi-treated sand when growing fungi for 10 days. The hydraulic conductivity reduction was attributed to the enhanced discontinuity of fluid channel by cross-linking and entangling mycelium network. Also, strong hydrophobicity of mycelium layer on the specimen surface contributed to the reduction of hydraulic conductivity. Scanning electron microscopy (SEM) imaging was conducted to assess the morphologies of sand matrix treated by fungi.

In some “pressure-sensitive” rock formations, the variation of permeability with pore pressure is sufficiently large that it needs to be accounted for. Accounting for this variation of permeability renders the pressure diffusion equation nonlinear, and not amenable to exact analytical solutions. A constitutive model that fits many data sets is that of a permeability that varies exponentially with pressure. One-dimensional flow to a constant-pressure boundary in a medium whose permeability varies exponentially with pressure is investigated in this work, using numerical and approximate approaches. As part of the analysis, a dimensionless “pressure-sensitivity” parameter β is defined as the logarithm of the ratio of the far-field permeability to the permeability at the outflow boundary. The problem can be approached by applying a Boltzmann transformation to the governing PDE, and converting the resultant ODE into an integral equation, which can be solved by an iterative procedure. This numerical solution can serve as a benchmark against which to judge the accuracy of approximate solutions. One simple approximate model is based on the “sorptivity approximation” developed by Philip for unsaturated flow, which can be adapted to any nonlinear diffusion problem. This model linearizes the problem by defining an effective permeability as a drawdown-weighted average of the permeability as a function of pore pressure. The Philip approximation predicts that the flowrate, normalized against the case in which the permeability is constant, is 1–0.333β, to first-order in β. This problem can also be approached as perturbation problem, with β as the perturbation parameter. The zeroth-order solution is the classical error-function solution for a 1D diffusion problem with a constant-pressure boundary. The first-order correction is obtained in closed form, from which the flowrate at the outflow boundary is found. To first-order, the normalized flowrate is 1-0.318β. This solution is extended to higher values of β by defining an effective permeability such that, if inserted into the analytical solution for the mathematically linear problem, yields a flowrate that is exact to first order in β. When compared to the numerical solution, this new result has 5% accuracy out to values of β = 2, which probably covers most cases of interest.