Numerous variations of Invasion-Percolation (IP) models can simulate multiphase flow in porous media across various scales (pore-scale IP to macroscopic IP); here, we are interested in gas flow in water-saturated porous media. This flow occurs either as continuous or discontinuous flow, depending on the flow rate and the porous medium’s nature. Literature suggests that IP models are well suited for the discontinuous gas flow regime; other flow regimes have not been explored. Our research compares four existing macroscopic IP models and ranks their performance in these “other” flow regimes. We test the models on a range of gas-injection in water-saturated sand experiments from transitional and continuous gas flow regimes. Using the light transmission technique, the experimental data is obtained as a time series of images in a 2-dimensional setup. To represent pore-scale heterogeneities, we ran each model version on several random realizations of the initial entry pressure field. We use a diffused version of the so-called Jaccard coefficient to rank the models against the experimental data. We average the Jaccard coefficient over all realizations per model version to evaluate each model and calibrate specific model parameters. Depending on the application domain, we observe that some macroscopic IP model versions are suitable in these previously unexplored flow regimes. Also, we identify that the initial entry pressure fields strongly affect the performance of these models. Our comparison method is not limited to gas-water systems in porous media but generalizes to any modelling situation accompanied by spatially and temporally highly resolved data.

For decades, seismic imaging methods have been used to study the critical zone, Earth's thin, life-supporting skin. The vast majority of critical zone seismic studies use traveltime tomography, which poorly resolves heterogeneity at many scales relevant to near-surface processes, therefore, limiting progress in critical zone science. Full-waveform inversion can overcome this limitation by leveraging more of the seismic waveform and enhancing the resolution of geophysical imaging. In this study, we apply full-waveform inversion to elucidate previously undetected heterogeneity in the critical zone at a well-studied catchment in the Laramie Range, Wyoming. In contrast to traveltime tomograms from the same data set, our results show variations in depth to bedrock ranging from 5 to 60 meters over lateral scales of just tens of meters and image steep low-velocity anomalies suggesting hydrologic pathways into the deep critical zone. Our results also show that areas with thick fractured bedrock layers correspond to zones of slightly lower velocities in the deep bedrock, while zones of high bedrock velocity correspond to sharp vertical transitions from bedrock to saprolite. By corroborating these findings with borehole imagery, we hypothesize that lateral changes in bedrock fracture density majorly impact critical zone architecture. Borehole data also show that our full-waveform inversion results agree significantly better with velocity logs than previously published traveltime tomography models. Full-waveform inversion thus appears unprecedently capable of imaging the spatially complex porosity structure crucial to critical zone hydrology and processes.

Optimization of spatially consistent parameter fields is believed to increase the robustness of parameter estimation and its transferability to ungauged basins. The current paper extends previous multi-objective and transferability studies by exploring the value of both multi-basin and spatial pattern calibration of distributed hydrologic models as compared to single-basin and single-objective model calibrations, with respect to tradeoffs, performance and transferability. The mesoscale Hydrological Model (mHM) is used across six large central European basins. Model simulations are evaluated against daily streamflow observations at the basin outlets and remotely sensed evapotranspiration patterns obtained with a two-source energy balance approach. Several model validation experiments are performed through combinations of single- (discharge) and multi-objective (discharge and spatial evapotranspiration patterns) calibrations with holdout experiments saving alternating basins for model evaluation. The study shows that there are very minimal tradeoffs between spatial and temporal performance objectives and that a joint calibration of multiple basins using multiple objective functions provides the most robust estimations of parameter fields that perform better when transferred to ungauged basins. The study indicates that particularly the multi-basin calibration approach is key for robust parametrizations, and that the addition of an objective function tailored for matching spatial patterns of ET fields alters the spatial parameter fields while significantly improving the spatial pattern performance without any tradeoffs with discharge performance. In light of model equifinality, the minimal tradeoff between spatial and temporal performance shows that adding spatial pattern evaluation to the traditional temporal evaluation of hydrological models can assist in identifying optimal parameter sets.

Effective flash flood forecasting and risk communication are imperative for mitigating the impacts of flash floods. However, the current forecasting of flash flood occurrence and magnitude largely depends on forecasters’ expertise. An emerging flashiness-intensity-duration-frequency (F-IDF) product is anticipated to facilitate forecasters by quantifying the frequency and magnitude of an imminent flash flood event. To make this concept usable, we develop two distributed F-IDF products across the contiguous US, utilizing both a Machine Learning (ML) approach and a physics-based hydrologic simulation approach that can be applied at ungaged pixels. Specifically, we explored 20 common ML methods and interpreted their predictions using the Shapley Additive exPlanations method. For the hydrologic simulation, we applied the operational flash flood forecast framework – EF5/CREST. It is found that: (1) both CREST and ML depict similar flash flood hot spots across the CONUS; (2) The ML approach outperforms the CREST-based approach, with the drainage area, air temperature, channel slope, potential evaporation, soil erosion identified as the five most important factors; (3) The CREST-based approach exhibits high model bias in regions characterized by dam/reservoir regulation, urbanization, or mild slopes. We discuss two application use cases for these two products. The CREST-based approach, with its dynamic streamflow predictions, can be integrated into the existing real-time flash flood forecast system to provide event-based forecasts of the frequency and intensity of floods at multiple durations. On the other hand, the ML-based approach, which is a static measure, can be integrated into a flash flood risk assessment framework for urban planners.

Authors and affiliationsChristopher Masafu1, Richard Williams1, Martin D. Hurst11School of Geographical and Earth Sciences, University of Glasgow, Glasgow, G12 8QQ, UKCorresponding Author: Christopher Masafu ([email protected])

Preferential groundwater discharge zones are critical to a wide range of surface water habitat and water quality processes, but they can be difficult to characterize due to strong spatial variability in flux rate and high attenuation of natural temperature signals. As such, passive heat-as-a-tracer methods employing Vertical Temperature Profiler data are often ill-suited for quantifying vertical discharge flux rates due to a combination of inadequate sensor distribution and resolution paired with analytical modeling methods based on diurnal signals only. Using data from a site of contaminant-loaded groundwater discharge to the Quashnet River on Cape Cod, Massachusetts, USA, we demonstrate how coupled improvements in instrumentation and parameter estimation methods can largely alleviate these issues. Consequently, more accurate groundwater flux estimates, including temporal variations, are now possible at sites of strong discharge using passive heat-as-a-tracer methods.

Streamflow is one of the most important variables in hydrology but is difficult to measure continuously. As a result, nearly all streamflow time series are estimated from rating curves that define a mathematical relationship between streamflow and some easy-to-measure surrogate like water-surface elevation (stage). Most ratings are still fit manually, which is time-consuming and subjective. To improve that process, the U.S. Geological Survey (USGS), among others, is evaluating algorithms to automate that fitting. Several automated methods already exist, and each parameterizes the rating curve slightly differently. Because of the nonconvex nature of the problem, those differences can greatly affect performance. After some trial and error, we settled on reparameterizing the classic segmented power law somewhat like a Bayesian physics-informed neural network. Being physics-informed and Bayesian, the algorithm requires minimal data and also estimates uncertainty. Its implementation is open source and easily modified so that others can contribute to improving the quality of USGS streamflow data.

In most, but not all of the scientific literature, cutting of forested watershed results in an increase in water yield of a watershed. In this study, a double-mass plot of the cumulative monthly flow of water between 1961 and 2000, from a 79,000 km2 (7.9 million ha) forested watershed feeding into the Mekong River, on cumulative monthly precipitation over the same period, was used to demonstrate a significant decrease in the water yield in 1985. For 10-12 years after 1985, the total water yield from the watershed decreased by 42% (256 mm) while the late (March and April) dry-season flow decreased by almost 80%. From the changes in water yield and an understanding of the local hydrology, we calculated that 75-80% of the forested area was cut, i.e. more than 6 million ha, implying that the decrease in total water yield from the area of the forest that was actually cut, was just over 50%, while the late dry-season flow from the same area was virtually eliminated. We consider that the main reason for the reduction in water yield, after the forest was cut was an immediate increase in dry-season transpiration by the remaining old forest, newly-exposed understorey and regrowth vegetation, all of which were considered to be accessing groundwater in the regolith. The amount of groundwater accessed was sufficient to allow the cut forest to lose water at the potential rate over the whole year. We conclude that restoration of the watershed water flows resulted mainly from forest regrowth.

A document by Timothy Hodson. Click on the document to view its contents.

The Eastern Nile Basin (ENB) countries of Egypt, Sudan, South Sudan, and Ethiopia are subject to pronounced water, energy, and food (WEF) insecurity problems. There is a need to manage the WEF nexus to meet rapidly increasing demands, but this is extremely challenging due to resource scarcity and climate change. If countries that rely on shared transboundary water resources have contradictory WEF plans, that could diminish the expected outcomes, both nationally and regionally. Egypt as the downstream Nile country is concerned about ongoing and future developments upstream, which could exacerbate Egypt’s water scarcity and affect its ability to meet its WEF objectives. In this context, we introduce a multi-model WEF framework that simulates the ENB’s water resources, food production, and hydropower generation systems. The models were calibrated and validated for the period 1983-2016, then utilized to project a wide range of future development plans, up to 2050, using four performance measures to evaluate the WEF nexus. A thematic pathway for regional development that showed high potential for mutual benefits was identified. Results indicate that the ENB countries could be nearly food self-sufficient before 2050 and generate an additional 42000 GWh/yr of hydropower, with minimal impacts on Egypt’s water scarcity problems. The WEF planning outcomes for the region are sensitive to climate change, but, if social drivers can be managed (e.g., by lowered population growth rates) despite the difficulties involved, climate change impacts on WEF security could be less severe.

Quantitative precipitation forecasting in numerical weather prediction (NWP) models rely on physical parameterization schemes. However, these schemes involve considerable uncertainties due to limited knowledge of the mechanisms involved in the precipitating process, ultimately leading to degraded precipitation forecasting skills. To address this issue, our study proposes using a Swin-Transformer based deep learning (DL) model to quantitatively map fundamental variables solved by NWP models to precipitation maps. Our results show that the DL model effectively extracts features over meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different driven synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed DL model can provide a vital reference for capturing precipitation-triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations.

Existing models for estimating hyporheic oxygen mass transfer often require numerous parameters related to flow, bed, and channel characteristics, which are frequently unavailable. We performed a meta-analysis on existing dataset, enhanced with high Reynolds number cases from a validated Computational Fluid Dynamics model, to identify key parameters influencing effective diffusivity at the sediment water interface. We applied multiple linear regression to generate empirical models for predicting eddy diffusivity. To simplify this, we developed two single-parameter models using either a roughness or permeability-based Reynolds number. These models were validated against existing models and literature data. The model using roughness Reynolds number is easy to use and can provide an estimate of the oxygen transfer coefficient, particularly in scenarios where detailed bed characteristics such as permeability might not be readily available.

Turbidity limits light availability in many glacier-fed lakes and reservoirs, with far-reaching ecological consequences. We use field observations and hydrodynamic modelling to examine the physical processes affecting turbidity in the epilimnion of a glacier-fed hydroelectric reservoir in response to changes in reservoir operations (e.g. water level, inflows and withdrawals), and to natural processes (e.g. particle settling, internal seiching and upwelling). The combination of cold inflows and deep outlets leads to plunging inflows and the isolation of the epilimnion; this isolation, along with particle settling, results in a remarkable clearing of the epilimnion during summer. We simulate a wide range of scenarios based on 46 years of historical flows. We find that the water level and inflow rate in spring control epilimnetic turbidity at the beginning of summer, and this turbidity is a primary determinant of the turbidity and light penetration for the rest of the summer. Turbidity during summer is also impacted by wind-driven thermocline motions. We examine these motions using wave characteristics diagrams and two-dimensional spectra and identify the period and wavelength of the two dominant wave modes: the fundamental internal seiche (\(\approx\) 4 days) and diurnally-forced waves. Occasionally, internal motions are large enough to upwell turbid metalimnetic water to the free surface at the upstream end of the reservoir. These upwelling events coincide with peaks in the inverse of the Wedderburn number. Pulses of upwelled water are advected downstream, setting up a longitudinal turbidity gradient.

Stochastic Watershed Models (SWMs) are an important innovation in hydrologic modeling that propagate uncertainty into model predictions by adding samples of model error to deterministic simulations. A growing body of work shows that univariate SWMs effectively reduce bias in hydrologic simulations, especially at the upper and lower flow quantiles. This has important implications for short term forecasting and the estimation of design events for long term planning. However, the application of SWMs in a regional context across many sites is underexplored. Streamflow across nearby sites is highly correlated, and so too are hydrologic model errors. Further, in arid and semi-arid regions streamflow can be intermittent, but SWMs rarely model zero flows at one site, let alone correlated intermittency across sites. In this technical note, we contribute a multisite SWM that captures univariate attributes of model error (heteroscedasticity, autocorrelation, non-normality, conditional bias), as well as multisite attributes of model error (cross-correlated error magnitude and persistence). The SWM also incorporates a multisite, auto-logistic regression model to account for multisite persistence in streamflow intermittency. The model is applied and tested in a case study that spans 14 watersheds in the Sacramento, San Joaquin, and Tulare basins in California. We find that the multisite SWM is able to better reproduce regional low and high flow events and design statistics as compared to a single-site SWM applied independently to all locations.

Central Asia (CA) is experiencing rapid warming, leading to more Extreme precipitation events (EPEs). However, the anticipated changes in cropland and population exposure to EPEs are still unexplored. In this study, projected changes in EPEs characteristics, as well as cropland and population exposure from EPEs are quantified using global climate model simulations. Our findings reveal a significant increase in the exposure of cropland and population to extreme precipitation over time. Specifically, under the high-emission SSP5-8.5 future pathway, the amount, frequency, intensity, and spatial extent of extreme precipitation in CA are projected to considerably amplify, particularly in the high mountain regions. Under the SSP5-8.5 scenario, cropland exposure in CA increases by 46.4%, with a total cropland exposure of approximately 190.7 million km² expected between 2021 and 2100. Additionally, under the SSP3-7.0 scenario, population exposure in CA increases by 92.6%, resulting in a total population exposure of about 48.1 billion person-days during the same period. The future maximum centers of exposure are concentrated over northern Kazakhstan and the tri-border region of Tajikistan, Kyrgyzstan, and Uzbekistan. Notably, the climate effect is more dominant than the other effects, whereas changes in population effect contribute to the total change in population exposure. Given the heterogeneous distribution of population and cropland in CA, it is imperative for the countries in the region to implement effective measures that harness extreme precipitation and cope with the impacts of these extreme climate events.

In recent years, the Sample Analysis at Mars (SAM) instrument on board the Mars Science Laboratory (MSL) Curiosity rover has detected methane variations in the atmosphere at Gale crater. Methane concentrations appear to fluctuate seasonally as well as sub-diurnally, which is difficult to reconcile with an as-yet-unknown transport mechanism delivering the gas from underground to the atmosphere. To potentially explain the fluctuations, we consider barometrically-induced transport of methane from an underground source to the surface, modulated by temperature-dependent adsorption. The subsurface fractured-rock seepage model is coupled to a simplified atmospheric mixing model to provide insights on the pattern of atmospheric methane concentrations in response to transient surface methane emissions, as well as to predict sub-diurnal variation in methane abundance for the northern summer period, which is a candidate time frame for Curiosity’s potentially final sampling campaign. The best-performing scenarios indicate a significant, short-lived methane pulse just prior to sunrise, the detection of which by SAM-TLS would be a potential indicator of the contribution of barometric pumping to Mars’ atmospheric methane variations.

Montane snowpack is a vital source of water supply in the Western United States. However, the future of snow in these regions in a changing climate is uncertain. Here, we use a large-ensemble approach to evaluate the consistency across 124 statistically downscaled snow water equivilent (SWE) projections between end-of-century (2076 – 2095) and early 21st century (2106 – 2035) periods. Comparisons were performed on dates corresponding with the end of winter (15 April) and spring snowmelt (15 May) in five western US montane domains. By benchmarking SWE climate change signals using the disparity between snow projections, we identified relationships between SWE projections that were repeatable across each domain, but shifted in elevation. In low to mid-elevations, 15 April average projected decreases to SWE of 48% or larger were greater than the disparity between models. Despite this, a significant portion of 15 April SWE volume (39 – 93%) existed in higher elevation regions where the disparities between snow projections exceeded the projected changes to SWE. Results also found that 15 April and 15 May projections were strongly correlated (r 0.82), suggesting that improvements to the spread and certainty of 15 April SWE projections would translate to improvements in later dates. The results of this study show that large-ensemble approaches can be used to measure coherence between snow projections and identify both 1) the highest-confidence changes to future snow water resources, and 2) the locations and periods where and when improvements to snow projections would most benefit future snow projections.

Climate change and nitrogen (N) pollution are altering biogeochemical and ecohydrological processes in dryland watersheds, increasing N export, and threatening water quality. While simulation models are useful for projecting how N export will change in the future, most models ignore biogeochemical “hotspots” that develop in drylands as moist microsites become hydrologically disconnected from plant roots when soils dry out. These hotspots enable N to accumulate over dry periods and rapidly flush to streams when soils wet up. To better project future N export, we developed a framework for representing hotspots using the ecohydrological model RHESSys. We then conducted a series of virtual experiments to understand how uncertainties in model structure and parameters influence N export. Modeled export was sensitive to the abundance of hotspots in a watershed, increasing linearly and then reaching an asymptote with increasing hotspot abundance. Peak streamflow N was also sensitive to a soil moisture threshold at which subsurface flow from hotspots reestablished, allowing N to be transferred to streams; it increased and then decreased with an increasing threshold value. Finally, N export was generally higher when water diffused out of hotspots slowly. In a case study, we found that when hotspots were modeled explicitly, peak streamflow nitrate export increased by 29%, enabling us to better capture the timing and magnitude of N losses observed in the field. This modeling framework can improve projections of N export in watersheds where hotspots play an increasingly important role in water quality.

A document by Robert Ladwig. Click on the document to view its contents.

Underground wastewater injection into deep reservoirs confronts complex hydrogeologic conditions potentially leading to induced seismicity. We focus on the geologic units of the Arbuckle Group carbonates in Oklahoma that are frequently used for disposing of produced water associated with oil and gas production. Subsurface pressures and fluid flow figure prominently in most explanations for induced seismicity and are important in evaluations of future storage potential of the Arbuckle. To understand subsurface pressure conditions within the Arbuckle we monitored the water levels in 15 inactive wells. The wells were monitored at 30-second intervals, with eight wells monitored since September 2016, and an additional seven from July 2017. All of the wells were monitored until early March 2020. Since 2016, 13 of the 15 wells showed a net decrease in well level (a.k.a. hydraulic head), proportional to near-borehole fluid pressure. The pressure patterns observed in each well vary from one another, and some wells display a gradual decrease in pressure over time, some have a rapid decrease, and others show irregular changes. The well pressures respond to Earth tides and injections into nearby wells as well as response to distal and proximal seismic waves, though responses vary considerably between wells. Moreover, there appears to be a threshold injection rate above which pressure changes level off. The data illustrate that Arbuckle hydrogeology is a multi-scale, temporally dynamic system, with regional heterogeneity of porosity and permeability. These dynamics exert important controls on seismic hazard and storage capacity estimates.

The value of seasonal streamflow forecasts for the hydropower industry has long been assessed by considering metrics related to hydropower availability. However, this approach overlooks the role played by hydropower dams within the power grid, therefore providing a myopic view of how forecasts could improve the operations of large-scale power systems. With the aim of understanding how the value of streamflow forecasts penetrates through the power grid, we developed a coupled-water energy model that is subject to reservoir inflow forecasts with different levels of accuracy. We implement the modelling framework on a real-world case study based on the Cambodian grid, which relies on hydropower, coal, oil, and imports from neighboring countries. In particular, we evaluate the performance in terms of metrics selected from both the reservoir and power systems, including available and dispatched hydropower, power production costs, CO2 emissions, and transmission line congestion. Through this framework, we demonstrate that streamflow forecasts can positively impact the operations of hydro-dominated power systems, especially during the transition from wet to dry seasons. Moreover, we show that the value largely varies with the specific metric of performance at hand as well as the level of operational integration between water and power systems.

Two-phase fluid flow in fractured porous media impacts many natural and industrial processes but our understanding of flow dynamics in these systems is constrained by difficulties measuring the flow in the interacting fracture and porous media. We present a novel experimental system that allows quantitative visualization of the air and water phases in a single analog fractured porous medium. The fracture system consists of a sintered-glass porous plate in contact with an impermeable glass plate. A reservoir connected to the porous plate allows control of pore pressure within the porous medium. The fracture fills and drains through the porous matrix and flow manifolds along two edges of the fracture. The fracture is mounted in an imaging system that includes a controlled light-emitting diode (LED) panel and a charge-coupled-device (CCD) camera. Flow and pressure are controlled and monitored by a computer during experiments. To demonstrate this system, we carried out a series of cyclic drainage and imbibition experiments in fractures bounded by porous media with different pore-size distributions in the porous matrix. Images of the drainage process demonstrate that the air-water distribution within the fracture evolves differently than has been observed in non-porous fractured systems. Specifically, we observed limited trapping of water within the fracture during drainage. Conversely, during imbibition, because air cannot exit through the porous matrix, significant regions of air became entrapped once pathways to the fracture boundaries became water filled. The differences in phase evolution led to substantial differences in the evolution of estimated relative permeability with saturation.

The complex interactions among soil, vegetation, and site hydrologic conditions driven by precipitation and tidal cycles control biogeochemical transformations and bi-directional exchange of carbon and nutrients across the terrestrial-aquatic interfaces (TAIs) in the coastal regions. This study uses a highly mechanistic model, ATS-PFLOTRAN, to explore how these interactions impact the material exchanges and carbon and nitrogen cycling along a TAI transect in the Chesapeake Bay region that spans zones of open water, coastal wetland and upland forest. Several simulation scenarios are designed to parse the effects of the individual controlling factors and the sensitivity of carbon cycling to reaction constants derived from laboratory experiments. Our simulations revealed a hot zone for carbon cycling under the coastal wetland and the transition zones between the wetland and the upland. Evapotranspiration is found to enhance the exchange fluxes between the surface and subsurface domains, resulting in higher dissolved oxygen concentration in the TAI. The transport of organic carbon decomposed from leaves provides additional source of organic carbon for the aerobic respiration and denitrification processes in the TAI, while the variability in reaction rates mediated by microbial activities plays a dominant role in controlling the heterogeneity and dynamics of the simulated redox conditions. This modeling-focused exploratory study enabled us to better understand the complex interactions of various system components at the TAIs that control the hydro-biogeochemical processes, which is an important step towards representing coastal ecosystems in larger-scale Earth system models.

Seasonal snowpack in the Western United States (WUS) is vital for meeting summer hydrological demands, reducing the intensity and frequency of wildfires, and supporting snow-tourism economies. While the frequency and severity of snow droughts (SD) are expected to increase under continued global warming, the uncertainty from internal climate variability remains challenging to quantify. Using a 30-member large ensemble from a state-of-the-art global climate model, the Seamless System for Prediction and EArth System Research (SPEAR), and an observations-based dataset, we find WUS SD changes are already significant. By 2100, SPEAR projects SDs to be nearly 9 times more frequent under shared socioeconomic pathway 5-8.5 (SSP5-8.5) and 5 times more frequent under SSP2-4.5. By investigating the influence of the two primary drivers of SD, temperature and precipitation amount, we find the average WUS SD will become warmer and wetter. To assess how these changes affect future summer water availability, we track April 15th snowpack across WUS watersheds, finding differences in the onset time of a “no-snow” threshold between regions and large internal variability within the ensemble that are both on the order of decades. For example, under SSP5-8.5, SPEAR projects California could experience no-snow anywhere between 2058 and 2096, while in the Pacific Northwest, the earliest transition happens in 2091. We attribute the inter-regional uncertainty to differences in the regions’ mean winter temperature and the intra-regional uncertainty to irreducible internal climate variability. This analysis indicates that internal climate variability will remain a significant source of uncertainty for WUS hydrology through 2100.