Deep learning (DL) methods have shown great promise for accurately predicting hydrologic processes but have not yet reached the complexity of traditional process-based hydrologic models (PBHM) in terms of representing the entire hydrologic cycle. The ability of PBHMs to simulate the hydrologic cycle makes them useful for a wide range of modeling and simulation tasks, for which DL methods have not yet been adapted. We argue that we can take advantage of each of these approaches to couple DL methods into PBHMs as individual process parameterizations. We demonstrate that this is viable by developing DL process parameterizations for turbulent heat fluxes and couple them into the Structure for Unifying Multiple Modeling Alternatives (SUMMA), a modular PBHM modeling framework. We developed two DL parameterizations and integrated them into SUMMA, resulting in a one way coupled implementation (NN1W) which relies only on model inputs and a two-way coupled implementation (NN2W), which also incorporates SUMMA-derived model states. Our results demonstrate that the DL parameterizations are able outperform calibrated standalone SUMMA benchmark simulations. Further we demonstrate that the two-way coupling can simulate the long-term latent heat flux better than the standalone benchmark. This shows that DL methods can benefit from PBHM information, and the synergy between these modeling approaches is superior to either approach individually.

Identifying fluid flow maldistribution in planar geometries is a well-established problem in subsurface science/engineering. Of particular importance to the thermal performance of Engineered (or “Enhanced”) Geothermal Systems (EGS) is identifying the existence of non-uniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a Genetic Algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two-dimensional Hele-Shaw geometry (i.e., parallel-plates). The inverse problem is solved by minimizing the L2-norm between simulated Residence Time Distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C-Dot nanoparticle). Principal Component Analysis (PCA) of spatially-correlated permeability fields enabled reduction of the parameter space by more than a factor of ten and restricted the inverse search to reservoir-scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding-plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. The results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.

Hydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi-arid landscapes. This along with data scarcity poses challenges for large-scale modelling of vegetation-water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5 km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasise limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co-variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter-intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide-spread occurrence of the “inverse texture effect”’ on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water-use strategy. Thus, our observation-based products have large potential for better understanding complex vegetation–water interactions from regional to continental scales.

The recent development of the TOUGH3 code allows for a faster and more reliable fluid flow simulator. At the same time, new versions of FLAC3D are released periodically, allowing for new features and faster execution. In this paper, we present the first implementation of the coupling between TOUGH3 and FLAC3Dv6/7, maintaining parallel computing capabilities for the coupled fluid flow and geomechanical codes. We compare the newly developed version with analytical solutions and with the previous approach, and provide some performance analysis on different meshes and varying the number of running processors. Finally, we present two case studies related to fault reactivation during CO2 sequestration and nuclear waste disposal. The use of parallel computing allows for meshes with a larger number of elements, and hence more detailed understanding of thermo-hydro-mechanical processes occurring at depth.

Fire regimes are influenced by both exogenous drivers (e.g., increases in atmospheric CO2; and climate change) and endogenous drivers (e.g., vegetation and soil/litter moisture), which constrain fuel loads and fuel aridity. Herein, we identified how exogenous and endogenous drivers can interact to affect fuels and fire regimes in a semiarid watershed in the inland northwestern U.S. throughout the 21st century. We used a coupled ecohydrologic and fire regime model to examine how climate change and CO2 scenarios influence fire regimes over space and time. In this semiarid watershed we found that, in the mid-21st century (2040s), the CO2 fertilization effect on vegetation productivity outstripped the effects of climate change-induced fuel decreases, resulting in greater fuel loading and, thus, a net increase in fire size and burn probability; however, by the late-21st century (2070s), climatic warming dominated over CO2 fertilization, thus reducing fuel loading and fire activity. We also found that, under future climate change scenarios, fire regimes will shift progressively from being flammability to fuel-limited, and we identified a metric to quantify this shift: the ratio of the change in fuel loading to the change in its aridity. The threshold value for which this metric indicates a flammability versus fuel-limited regime differed between grasses and woody species but remained stationary over time. Our results suggest that identifying these thresholds in other systems requires narrowing uncertainty in exogenous drivers, such as future precipitation patterns and CO2 effects on vegetation.

The difference between precipitation and evaporation has been extensively used to characterize the water cycle’s response to global warming. However, when it comes to the global scale, the information provided by this metric is inconclusive. Herein, we discuss how the sum of precipitation and evaporation could complement the assessment of global water cycle intensification. To support our argument, we present a brief yet robust correlation analysis of both metrics in four reanalysis data sets (20CR v3, ERA-20C, ERA5, and NCEP/NCAR R1). Additionally, by combining the two metrics, we investigate how well the global water cycle fluxes are represented in the four reanalyses. Among them, we observe four different responses to the temperature increase between 1950-2010, with ERA5 showing the best agreement with the intensification hypothesis. We argue that these discrepancies would remain elusive with the traditional approach, which makes the utilization of the sum of precipitation and evaporation a valuable addition to our methodological toolbox for the assessment of the global water cycle intensification.

We investigate if the commonly neglected riverine detrital carbonate fluxes might balance several chemical mass balances of the global ocean. Particulate inorganic carbon (PIC) concentrations in riverine suspended sediments, i.e., carbon contained by these detrital carbonate minerals, was quantified at the basin and global scale. Our approach is based on globally representative datasets of riverine suspended sediment composition, catchment properties and a two-step regression procedure. The present day global riverine PIC flux is estimated at 3.1 ± 0.3 Tmol C/y (13% of total inorganic carbon export and 4 % of total carbon export), with a flux-weighted mean concentration of 0.26 ± 0.03 wt%. The flux prior to damming was 4.1 ± 0.5 Tmol C/y. PIC fluxes are concentrated in limestone-rich, rather dry and mountainous catchments of large rivers in Arabia, South East Asia and Europe with 2.2 Tmol C/y (67.6 %) discharged between 15 °N and 45 °N. Greenlandic and Antarctic meltwater discharge and ice-rafting additionally contribute 0.8 ± 0.3 Tmol C/y. This amount of detrital carbonate minerals annually discharged into the ocean implies a significant contribution of calcium (~ 4.75 Tmol Ca/y) and alkalinity fluxes (~ 10 Tmol(eq)/y) to marine mass balances and moderate inputs of strontium (~ 5 Gmol Sr/y), based on undisturbed riverine and cryospheric inputs and a dolomite/calcite ratio of 0.1. Magnesium fluxes (~ 0.25 Tmol Mg/y), mostly hosted by less-soluble dolomite, are rather negligible. These unaccounted fluxes help elucidating respective marine mass balances and potentially alter conclusions based on these budgets.

Floods are convincingly the most frequent and widespread natural hazard across the world. With an ample amount of literature forecasting increase in its frequency and magnitude further in the future, highly credible and efficient algorithms and tools are crucial for real-time flood monitoring. In this study, a highly efficient tool, Multi-Mission Flood Mapper, has been developed to delineate flood inundation extent without any human intervention from SAR images captured by multiple microwave SAR satellite missions, including ALOS PALSAR CEOS, ALOS 2 CEOS, COSMO-SkyMed, ENVISAT ASAR, ERS 1/2 CEOS, ERS 1/2 SAR(.E1, .E2), ICEYE, JERS CEOS, KOMPSAT-5, PAZ, RADARSAT-1 & -2, RCM, SAOCOM, SeaSat, Sentinel-1, TerraSAR-X, and TanDEM-X. The efficacy of the developed tool is assessed by performing a test on a significant number of flood events in India having diverse flooding patterns and landforms. To manifest the performance of the tool, the step-by-step processing at the backend of the tool is discussed in detail in this study by taking a flood event along the Ganga River in India as a case study. The algorithm of the tool includes various processing steps: pre-processing that incorporate applying orbit file, calibrate to sigma naught, speckle filtering, terrain correction and linear to decibel conversion; thematic analysis that involves multi-segmentation and Otsu’s thresholding techniques; post-processing that consists of the elimination of hill shadows, applying majority filter, and masking out permanent water bodies. Thus derived flood inundation layer is observed to be highly accurate compared to the master image. The total time taken by the tool for processing is about 4 minutes for the given image. The developed tool would be beneficial for rapid flood inundation map generation on a timely basis for flood monitoring and relief management during a disaster. In addition, the flood inundation layers can also be used for calibration/validation of hydrological/hydraulic models, geospatial planning, and generating flood hazard maps. Also, the Multi-Mission Flood Mapper tool is facilitated with a user-friendly Graphical User Interface (GUI), making it look simple and easy to use.

This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them. Each commentary focuses on a different perspective as follows: (i) field, experimental, remote sensing, and real-time data research and application (Section 1); (ii) Inclusive, equitable, and accessible science: Involvement, challenges, and support of early career, marginalized racial groups, women, LGBTQ+, and/or disabled researchers (Section 2); and (iii) an ICON perspective on machine learning for multiscale hydrological modeling (Section 3). Hydrologists depend on data monitoring, analyses, and simulations from these diverse scientific disciplines to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (in-situ: lab, plots, and field experiments) and secondary sources (ex-situ: remote sensing, UAVs, hydrologic models) that are typically openly available and discoverable. Hydrology-oriented organizations have pushed our community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. With increasing amounts of data, it has become difficult to decipher various complex hydrologic processes. However, machine learning, a branch of artificial intelligence, provides accurate and faster alternatives to understand different biogeochemical and hydrological processes better. Diversity, equity, and inclusivity are essential in terms of outreach and integration of peoples with historically marginalized identities into this professional discipline and respecting and supporting the local environmental knowledge of water users.

Poleward ocean heat transport is a key process in the earth system. We detail and review the northward Atlantic Water (AW) flow, Arctic Ocean heat transport, and heat loss to the atmosphere since 1900 in relation to sea ice cover. Our synthesis is largely based on a sea ice-ocean model forced by a reanalysis atmosphere (1900-2018) corroborated by a comprehensive hydrographic database (1950-), AW inflow observations (1996-), and other long-term time series of sea ice extent (1900-), glacier retreat (1984-) and Barents Sea hydrography (1900-). The Arctic Ocean, including the Nordic and Barents Seas, has warmed since the 1970s. This warming is congruent with increased ocean heat transport and sea ice loss and has contributed to the retreat of marine-terminating glaciers on Greenland. Heat loss to the atmosphere is largest in the Nordic Seas (60% of total) with large variability linked to the frequency of Cold Air Outbreaks and cyclones in the region, but there is no long-term statistically significant trend. Heat loss from the Barents Sea (~30%) and Arctic seas farther north (~10%) is overall smaller, but exhibit large positive trends. The AW inflow, total heat loss to the atmosphere, and dense outflow have all increased since 1900. These are consistently related through theoretical scaling, but the AW inflow increase is also wind-driven. The Arctic Ocean CO2 uptake has increased by ~30% over the last century - consistent with Arctic sea ice loss allowing stronger air-sea interaction and is ~8% of the global uptake.

Future precipitation changes are controlled by the atmospheric energy budget, with temperature, water vapor, and absorbing aerosols playing dominant roles in driving radiative changes. Atmospheric energy budgets are calculated for different Shared Socioeconomic Pathways (SSPs) using ScenarioMIP projections from phase 6 of the Climate Model Intercomparison Project and are used to quantify the influence of 21st century aerosol cleanup on precipitation. Absorbing aerosol influences on shortwave absorption are isolated from the effects of water vapor. Apparent hydrologic sensitivity is ~40% higher for the “Middle of the Road” (SSP2-4.5) scenario with aerosol cleanup than for the “Regional Rivalry” (SSP3-7.0) scenario that maintains aerosol. Regionally, cleanup-induced changes in the atmospheric energy budget are of a similar magnitude to the precipitation increases themselves and are larger than the influence of changes in atmospheric circulation. Policy choices about future absorbing aerosol emissions will therefore have major impacts on global and regional precipitation changes.

Torrential rainfall and rapid snowmelt in April 2017 caused deadly floods in northwestern Iran. An atmospheric river (AR), propagating across the Middle East and North Africa, was found responsible for this extreme event. The snowmelt was triggered by precipitation and warm advection associated with the AR. Total satellite-based rainfall for April 2017 was moderately below normal, suggesting that a heavy flood can happen during dry years. The AR was fed by moisture from the Mediterranean and Red Seas. Despite its adverse societal consequences, this event was beneficial to the recovery of the desiccating Lake Urmia. The impacts of this AR were not limited to flooding; it also facilitated dust transport to the region. This distinct characteristic of the ARs in the Middle East is attributed to major mineral dust sources located along their pathways. This event was reasonably predicted at 7-day lead time, crucially important for successful early warning systems.

Diverse, complex data are a significant component of Earth Science’s “big data” challenge. Some earth science data, like remote sensing observations, are well understood, are uniformly structured, and have well-developed standards that are adopted broadly within the scientific community. Unfortunately, for other types of Earth Science data, like ecological, geochemical and hydrological observations, few standards exist and their adoption is limited. The synthesis challenge is compounded in interdisciplinary projects in which many disciplines, each with their own cultures, must synthesize data to solve cutting edge research questions. Data synthesis for research analysis is a common, resource intensive bottleneck in data management workflows. We have faced this challenge in several U.S. Department of Energy research projects in which data synthesis is essential to addressing the science. These projects include AmeriFlux, Next Generation Ecosystem Experiment (NGEE) - Tropics, Watershed Function Science Focus Area, Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE), and a DOE Early Career project using data-driven approaches to predict water quality. In these projects, we have taken a range of approaches to support (meta)data synthesis. At one end of the spectrum, data providers apply well-defined standards or reporting formats before sharing their data, and at the other, data users apply standards after data acquisition. As these projects continue to evolve, we have gained insights from these experiences, including advantages and disadvantages, how project history and resources led to choice of approach, and enabled data harmonization. In this talk, we discuss the pros and cons of the various approaches, and also present flexible applications of standards to support diverse needs when dealing with complex data.

The accuracy of hydrological model predictions is limited by uncertainties in model structure and parameterization, and observations used for calibration, validation and model forcing. While calibration is usually performed with discharge estimates, the internal model processes might be misrepresented, and the model might be getting the “right results for the wrong reasons”, thus compromising model reliability. An alternative is to calibrate model parameters with remote sensing (RS) observations of the water cycle. Previous studies highlighted the potential of RS-based calibration to improve discharge estimates, focusing less on other variables of the water cycle. In this study, we analyzed in detail the contribution of five RS-based variables (water level (h), flood extent (A), terrestrial water storage (TWS), evapotranspiration (ET) and soil moisture (W)) to calibrate a coupled hydrologic-hydrodynamic model for a large Amazon sub-basin with extensive floodplains. Single-variable calibration experiments with all variables were able to improve discharge KGE from around 6.1% to 52.9% when compared to a priori parameter sets. Water cycle representation was improved with multi-variable calibration: KGE for all variables were improved in the evaluation period. By analyzing different calibration setups, a consistent selection of complementary variables for model calibration resulted in a better performance than incorporating all RS variables into the calibration. By looking at multiple RS observations of the water cycle, inconsistencies in model structure and parameterization were found, which would remain unknown if only discharge observations were considered.

Cosmic ray neutron sensors (CRNS) allow to determine field-scale soil moisture content non-invasively due to the dependence of aboveground measured epithermal neutrons on the amount of hydrogen. Because other pools besides soil contain hydrogen (e.g. biomass), it is necessary to consider these for accurate soil moisture content measurements, especially when they are changing dynamically (e.g., arable crops, de- and reforestation). In this study, we compare four approaches for the correction of biomass effects on soil moisture content measurements with CRNS using experiments with three crops (sugar beet, winter wheat and maize) on similar soils: I) site-specific functions based on in-situ measured biomass, II) a generic approach, III) the thermal-to-epithermal neutron ratio (Nr) and IV) the thermal neutron intensity. Calibration of the CRNS during bare soil conditions resulted in root mean square errors (RMSE) of 0.097, 0.041 and 0.019 m3/m3 between estimated and reference soil moisture content of the cropped soils, respectively. Considering in-situ measured biomass for correction reduced the RMSE to 0.015, 0.018 and 0.009 m3/m3. When thermal neutron intensity was considered for correction, similarly accurate results were obtained. Corrections based on Nr and the generic approach were less accurate. We also explored the use of CRNS for biomass estimation. The use of Nr only provided accurate biomass estimates for sugar beet. However, significant site-specific relationships between biomass and thermal neutron intensity were obtained for all three crops. It was concluded that thermal neutron intensity can be used to correct soil moisture content estimates from CRNS and to estimate biomass.

Understanding sensitive wetlands often requires non-invasive methods to characterize their complex geological structure and hydrogeological parameters. Here, geoelectrical characterization is explored by employing frequency-domain electromagnetic induction (EMI) at a site previously characterized by extensive intrusive measurements and 3D electrical resistivity tomography (ERT). This work investigates the performance of several approaches to obtain structural information from EMI data and sharp and smooth inversions. Additionally, the hydrological information content of EMI data is investigated using correlation with piezometric measurements, established petrophysical relationships, and synthetic modeling. EMI measurements were dominated by peat thickness and were relatively insensitive to both topography and depth to bedrock. An iso-conductivity method for peat depth estimation had a normalized mean absolute difference (NMAD) of 23.5%, and although this performed better than the sharp inversion algorithm (NMAD = 73.5%), a multi-linear regression approach achieved a more accurate prediction with only 100 measurements (NMAD = 17.8%). In terms of hydrological information content, it was not possible to unravel correlation causation at the site, however, synthetic modeling demonstrates that the EMI measurements are predominantly controlled by the electrical conductivity of the upper peat pore-water and not the thickness of the unsaturated zone or the lower peat pore-water conductivity. Additionally, a priori information significantly improves the potential for time-lapse applications in similar environments. This study provides an objective overview and insights for future EMI applications in similar environments. It also covers areas seldom investigated in EMI studies, e.g. error quantification and the depth of investigation of ERT models used for EMI calibration.

Small freshwater reservoirs are ubiquitous and likely play an important role in global greenhouse gas (GHG) budgets relative to their limited water surface area. However, constraining annual GHG fluxes in small freshwater reservoirs is challenging given their footprint area and spatially and temporally variable emissions. To quantify the GHG budget of a small (0.1 km2) reservoir, we deployed an eddy covariance system in a small reservoir located in southwestern Virginia, USA over two years to measure carbon dioxide (CO2) and methane (CH4) fluxes near-continuously. Fluxes were coupled with in situ sensors measuring multiple environmental parameters. Over both years, we found the reservoir to be a large source of CO2 (633-731 g CO2-C m-2 yr-1) and CH4 (1.02-1.29 g CH4-C m-2 yr-1) to the atmosphere, with substantial sub-daily, daily, weekly, and seasonal timescales of variability. For example, fluxes were substantially greater during the summer thermally-stratified season as compared to the winter. In addition, we observed significantly greater GHG fluxes during winter intermittent ice-on conditions as compared to continuous ice-on conditions, suggesting GHG emissions from lakes and reservoirs may increase with predicted decreases in winter ice-cover. Finally, we identified several key environmental variables that may be driving reservoir GHG fluxes at multiple timescales, including, surface water temperature and thermocline depth followed by fluorescent dissolved organic matter. Overall, our novel year-round eddy covariance data from a small reservoir indicate that these freshwater ecosystems likely contribute a substantial amount of CO2 and CH4 to global GHG budgets, relative to their surface area.

The future of Arctic social systems and natural environments is highly uncertain. Climate change will lead to unprecedented phenomena in the pan-Arctic region, such as regular shipping traffic through the Arctic Ocean, urban growth, military activity, expanding agricultural frontiers, and transformed Indigenous societies. While intergovernmental to local organizations have produced numerous synthesis-based visions of the future, a challenge in any scenario exercise is capturing the ‘possibility’ space of change. In this work, we employ a computational text analysis to generate unique thematic input for novel, story-based visions of the Arctic. Specifically, we develop a corpus of more than 2,000 articles in publicly accessible, English-language Arctic newspapers that discuss the future in the Arctic. We then perform a latent Dirichlet allocation, resulting in ten distinct topics and sets of associated keywords. From these topics and keywords, we design ten story-based scenarios employing the Mānoa mashup, science fiction prototyping, and other methods. Our results demonstrate that computational text analysis can feed directly into a creative futuring process, whereby the output stories can be traced clearly back to the original topics and keywords. We discuss our findings in the context of the broader field of Arctic scenarios and show that the results of this computational text analysis produce complementary stories to the existing scenario literature. We conclude that story-based scenarios can provide vital texture toward understanding the myriad possible Arctic futures.

Bed particles were tracked using passive integrated transponder (PIT) tags in a wandering reach of the San Juan River, British Columbia, Canada, to assess particle movement around three major bars in the river. In-channel topographic changes were monitored through repeat LiDAR surveys during this period and used in concert with the tracer dataset to assess the relationship between particle displacements and changes in channel morphology, specifically, the development and re-working of bars. This has direct implications for virtual velocity and morphologic based estimates of bedload flux, which rely on accurate estimates of the variability and magnitude of particle path lengths over time. Tracers were deployed in the river at three separate locations in the Fall of 2015, 2016, 2017 and 2018, with recovery surveys conducted during the summer low-flow season the year after tracer deployment and multiple mobilising events. Tracers exhibited path length distributions reflective of both morphologic controls and year to year differences related to the annual flow regime. Annual tracer transport was restricted primarily to less than one riffle-pool-bar unit, even during years with a greater number of peak floods and duration of competent flow . Tracer deposition and burial was focused along bar margins, particularly at or downstream of the bar apex, reflecting the downstream migration and lateral bar accretion observed on Digital Elevation Models (DEMs) of difference. This highlights the fundamental importance of bar development and re-working underpinning bedload transport processes in bar-dominated channels.

Managing river temperature in highly urbanized stream systems is critical for maintaining aquatic ecosystems and associated beneficial uses. Elevated river temperatures arise from warm surface inflows from impervious areas, channelization, the absence of riparian forests, and the lack of groundwater-surface water interactions. In the current work, we utilize a mechanistic river temperature model, i-Tree Cool River, to evaluate the cooling impacts of alternative ecological restoration scenarios: a) shading effects of tree planting in riparian areas and b) alternative streambed materials. The model was calibrated and validated on a 4.2 km reach of the Compton Creek in the Los Angeles (LA) River watershed, California, for low and high flow periods. The Arroyo Chub and Stickleback were considered the target species for analyzing thermal habitat suitability. River temperature simulations showed that like the ambient air temperature. The thermal response of the river in high flow periods was a function of upstream river temperature , where in low flow periods river water temperature was most affected by the tested restoration scenarios. Tree planting in the riparian corridor decreased the median thermal metrics: Max Weekly Max, Max Weekly Average, and Min Weekly Min Temperatures by an average of 3 ℃ (13%) to 20.4 ℃, 19.7 ℃, and 17.8 ℃, respectively. Using limecrete as an alternative bed material to the current concrete bottom decreased the median thermal metrics by an average of 0.9 ℃ (4%) to 22.7 ℃, 22 ℃, and 19 ℃, respectively. Combining the two scenarios decreased the river temperature metrics by an average of 4 ℃ (18%) to 18.2 ℃. Besides riparian vegetation, altering bed material is an impactful option in case of groundwater contamination and if channelized urban corridors lack the substrate to support vegetation. The use of ecological restoration scenarios resulted in summertime temperatures were within the documented spawning temperature thresholds and therefore temperature would not be a limiting factor in the potential reintroduction of the Arroyo Chub and Stickleback to Compton Creek. This tributary could be considered as a potential refuge and improved fish habitat in the LA basin during low flow periods.

Existing tidal input reduction approaches applied in accelerated morphodynamic simulations aim to capture the dominant tidal forces in a single or double representative tidal cycle, often referred to as a “morphological tide”. These heavily simplified tidal signals fail to represent the tidal extremes, and hence poorly allow to represent hydrodynamics above the intertidal areas. Here, a generic method is developed to construct a synthetic spring-neap tidal cycle that (1) represents the original signal; (2) is exactly periodic; and (3) is constructed directly from full-complexity boundary information. The starting point is a fortnightly modulation of the semi-diurnal tide to represent spring-neap variation, while conserving periodicity. Diurnal tides and higher harmonics of the semi-diurnal tide are included to represent the asymmetry of the tide. The amplitudes and phases are then adjusted to give a best fit to histograms of water levels and water level gradients. A depth-averaged model of the Ems estuary (The Netherlands) demonstrates the effects of alternative tidal input reduction techniques. Adopting the new approach, the shape of the tidal wave is well-represented over the entire length of the estuary, leading to an improved representation of extreme tidal conditions. In particular, representing intertidal dynamics benefits from the new approach, which is reflected by hydrodynamics and residual sand transport patterns that approach non-schematized tidal dynamics. Future morphodynamic simulations forced with the synthetic signal are expected to show a more realistic exchange of sediment between the channels and tidal flats, likely improving their overall predictive capacity.

Volcanic crises are often associated with magmatic intrusions or pressurization of magma chambers of various shapes. These volumetric sources deform the country rocks, changing their density, and cause uplift. Both the net mass of intruding magmatic fluids and these deformation effects contribute to surface gravity changes. Thus, to estimate the intrusion mass from gravity changes the deformation effects must be accounted for. We develop analytical solutions and computer codes for the gravity changes caused by triaxial sources of expansion. This establishes coupled solutions for joint inversions of deformation and gravity changes. Such inversions can constrain both the intrusion mass and the deformation source parameters more accurately.

Important progress has been made in recent years in characterizing surface soil moisture (SSM) at regional scales, through remote sensing estimates and the implementation of new in situ networks. Each of these sources of information has intrinsic features, such as the dynamic range of the SSM and the temporal frequency of acquisition. Another relevant factor is the period of data availability. Improving the knowledge of the limitations and biases of these features is crucial to increase the potential and the consistency of data sources validations. As a case of study we considered an agricultural area in the Argentinean Pampas, characterized by a sub-humid climate with a marked seasonal dynamic. It also holds a synchronized cropping rhythm and is subject to flooding and waterlogging that can last from days to months. The features mentioned above and considering that the region is almost devoid of irrigation, offer a natural laboratory that is distinguished by a wide dynamic range of SSM conditions. In this context, we analyze and expose different sources of SSM data gaps over long periods of time, using information from in situ stations and from the SMOS and SMAP satellite systems, during 2015-2019. We found SMAP data gaps resulting from the filtering of high SSM signals that are not spurious but typical for this flood-prone region. Reports from national institutions and comparison with other data sources allowed us to identify that high soil water content in the same period in which the data gaps occurred. In a different way, the SMOS register has a low-frequency range of data due to radio frequency interference over the study area. This data gap occurs during a long-anomalously wet period and it is relevant to take it into account when analyzing SMOS data for the full period. Our study shows the importance of using multiple sources of information and the relevance of examining the availability of data.

We present a new computational fluid dynamics approach to simulating two-phase flow in hybrid systems containing solid-free regions and deformable porous matrices. Our approach is based on the derivation of a unique set of volume-averaged partial differential equations that asymptotically approach the Navier-Stokes Volume-of-Fluid equations in solid-free-regions and multiphase Biot Theory in porous regions. The resulting equations extend our recently developed Darcy-Brinkman-Biot framework to multiphase flow. Through careful consideration of interfacial dynamics (relative permeability and capillary effects) and extensive benchmarking, we show that the resulting model accurately captures the strong two-way coupling that is often exhibited between multiple fluids and deformable porous media. Thus, it can be used to represent flow-induced material deformation (swelling, compression) and failure (cracking, fracturing). The model’s open-source numerical implementation, hybridBiotInterFoam, effectively marks the extension of computational fluid mechanics into modeling multiscale multiphase flow in deformable porous systems. The versatility of the solver is illustrated through applications related to material failure in poroelastic coastal barriers and surface deformation due to fluid injection in poroplastic systems.