“Reliable water supply” does not have a clear definition in the Western United States, where water resources are limited and such a definition would be especially useful. In Utah, the three water agencies and 500 public water systems have no consistent method to define, evaluate, and report it, potentially leading to an inability to meet regulatory water demands. We propose a unified definition of reliable water supply for Utah’s public water suppliers that can also be used elsewhere. The qualitative definition we propose is necessary to precede quantitative evaluations, set policy, and provide consistency to water resources management. We derive our definition from a two-part qualitative analysis: 1) an extensive review of existing definitions in industry and academia and 2) semi-structured interviews with managers of six diverse Utah water utilities. We propose that water supply be defined by three overlapping components—hydrology, infrastructure, and governance—and that reliability be defined by the capacity of the limiting component.

River bedforms influence fluvial hydraulics by altering bed roughness. With increasing flow velocity, subaqueous bedforms transition from flat beds to ripples, dunes, and an Upper Stage Plane Bed. Although prior research notes increased bedform height variation with flow strength and rapid shifts between bed configurations, the latter remains understudied. This study reanalyzes data from earlier experiments, and reveals a bimodal distribution of dune heights emerges beyond a transport stage of 18. Dune heights flicker between a low and high alternative state, indicating critical transitions. Potentially triggered by local sediment outbursts, these shifts lead to dune formation before returning to an Upper Stage Plane Bed. This flickering behavior challenges the adequacy of a single snapshot to capture the system's state, impacting field measurements and experimental designs, and questions the classical equilibrium equations. This study calls for further research to understand and quantify flickering behavior in sediment beds at high transport stages.

The escalating impact of climate change underscores the need for precise and timely forecasts of meteorological phenomena, particularly droughts, due to their extensive effects on agriculture, water resources, and ecosystems. Addressing this, we introduce a deep learning framework that merges Computer Vision with modified Transformer networks, tailored to predict future drought conditions leveraging historical global climate data. Our model inputs are stacked monthly global maps of Sea Surface Temperature, Temperature 2m above ground, and Total Precipitation, each spanning a year, thus creating a 36-channel input to capture seasonal variability.This study extends conventional Vision Transformers (ViT) by adapting them for sequence processing, enabling the model to learn the intricate temporal dynamics and spatial interdependencies inherent in climate data. By employing a sliding window approach, the model assimilates a sequence length of 12 months for each variable, and the target variables are stacks of Standardized Precipitation & Evapotranspiration Index (SPEI). Our modified ViT architecture successfully integrates the temporal sequencing by adjusting convolutional patch embeddings and positional embeddings, rendering the model sensitive to both the chronological progression and spatial distribution of climatic factors. Preliminary evaluations indicate the model's robust capability in forecasting drought conditions on a global scale. We substantiate these findings with performance metrics that illustrate the model's efficacy in interpreting and predicting the complex, non-linear, and non-stationary patterns of drought phenomena.

We used computer vision (U-Net) model to leverage Standardized Precipitation Evapotranspiration Index (SPEI), Google Trends Search Interest, and Twitter data to understand patterns with which people in Continental United States (CONUS) indicate awareness of and interest in droughts. We found significant statistical relationships between the occurrence of meteorological droughts, as measured by SPEI, and search interest on drought topics over CONUS. SI tends to lag meteorological drought by a period of 2-3 months, however relationships between meteorological drought and corresponding search interest varies significantly over CONUS in both space and time. People in states with increasingly drier conditions generally have become increasingly interested in drought topics. However, with worsening drought conditions in California, public search interest on drought topics in the state has not increased significantly between 2016 and 2020, despite the overall search interest being high. We additionally applied sentiment analysis on 5 million tweets related to droughts and found that public emotions towards drought have become more polarized over time.

Abstract: Globally, more people are impacted by extreme hydrologic events such as flooding than all other types of natural disasters combined, and the effects can be devastating. Two examples are the 2012 and 2022 floods along the Niger and Benue Rivers within the Lower Niger River Basin (LNRB) in Nigeria. Flooding within the LNRB typically occurs annually during the rainy season, however, the 2012 and 2022 flood events were of similar magnitude, had catastrophic socioenvironmental impacts, and occurred one decade apart. Limited historical gage data along the Niger and Benue Rivers precludes traditional flood frequency analysis in the LNRB. Hence, this study seeks to utilize globally available observations from satellite remote sensing to compute flood depths using the Floodwater Depth Estimation Tool (FwDETv2.1 version) implemented in Google Earth Engine. Other hydrological and physiographic characteristics of LNRB in 2012 and 2022 are also evaluated using remote sensing observations. Since the FwDET requires only globally available input data (flood inundation map and Digital Elevation Model) which favors data-sparse regions such as Nigeria, the potential for the FwDET tool to automatically quantify flood water depths, an important variable in flood frequency estimation and damage assessment, can be analyzed even when historical observations are lacking. The utility of the FwDETv2.1 for flood management and mitigation studies along global rivers with limited historical data is discussed. ReferenceIdowu, Dorcas, and Wendy Zhou. "Performance evaluation of a potential component of an early flood warning system—A case study of the 2012 flood, Lower Niger River Basin, Nigeria." Remote Sensing 11.17 (2019): 1970.Brakenridge, G. R., Kettner, A. J., Paris, S., Cohen, S., Nghiem, S. V. , River and Reservoir Watch Version 4.5, Satellite-based river discharge and reservoir area measurements, DFO Flood Observatory, University of Colorado, USA. http://floodobservatory.colorado.edu/ SiteDisplays/ 20.htm (Accessed 6 December 2023).Cohen, S.; Peter, B.G.; Haag, A.; Munasinghe, D.; Moragoda, N.; Narayanan, A.; May, S. Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections. Remote Sens. 2022, 14, 5313. https://doi.org/10.3390/rs14215313.B. G. Peter, S. Cohen, R. Lucey, D. Munasinghe, A. Raney and G. R. Brakenridge, "Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for Rapid and Large Scale Flood Analysis," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 1501005, doi: 10.1109/LGRS.2020.3031190.Brakenridge, G. Robert, Son V. Nghiem, and Zsofia Kugler. "Passive microwave radiometry at different frequency bands for river discharge retrievals." Earth and Space Science 10.8 (2023): e2023EA002859.Idowu, Dorcas. Assessing the Utilization of Remote Sensing and GIS Techniques for Flood Studies and Land Use/Land Cover Analysis Through Case Studies in Nigeria and the USA. Diss. Colorado School of Mines, 2021.Idowu, Dorcas, and Wendy Zhou. "Global Megacities and Frequent Floods: Correlation between Urban Expansion Patterns and Urban Flood Hazards." Sustainability 15.3 (2023): 2514.

Knowing where and when rivers cease to flow provides an important basis for evaluating riverine biodiversity, biogeochemistry and ecosystem services. We present a novel modeling approach to estimate monthly time series of streamflow intermittence at high spatial resolution at the continental scale. Streamflow intermittence is quantified at more than 1.5 million river reaches in Europe as the number of no-flow days grouped into five classes (0, 1-5, 6-15, 16-29, 30-31 no-flow days) for each month from 1981 to 2019. Daily time series of observed streamflow at 3706 gauging stations were used to train and validate a two-step Random Forest modeling approach. Important predictors were derived from time series of monthly streamflow at 73 million 15 arc-sec (~500 m) grid cells that were computed by downscaling the 0.5 arc-deg (~55 km) output of the global hydrological model WaterGAP, which accounts for human water use. Of the observed perennial and intermittent station-months, 97.8% and 86.4%, respectively, are correctly predicted. Interannual variations of the number of intermittent months at intermittent reaches are satisfactorily simulated, with a median Pearson correlation of 0.5. While the spatial prevalence of intermittent reaches is underestimated, the number of intermittent months is overestimated in dry regions of Europe where artificial storage abounds. Our model estimates that 3.8% of all European reach-months and 17.2% of all reaches were intermittent during 1981-2019, predominantly with 30-31 no-flow days. Although estimation uncertainty is high, our study provides, for the first time, information on the continent-wide dynamics of intermittent rivers and streams.

In the recent past, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor GRACE Follow-On (GRACE-FO), have become invaluable tools for characterizing drought through measurements of Total Water Storage Anomaly (TWSA). However, the existing approaches have often overlooked the uncertainties in TWSA that stem from GRACE orbit configuration, background models, and intrinsic data errors. Here we introduce a fresh view on this problem which incorporates the uncertainties in the data: the Probabilistic Storage-based Drought Index (PSDI). Our method leverages Monte Carlo simulations to yield realistic realizations for the stochastic process of the TWSA time series. These realizations depict a range of plausible drought scenarios that later on are used to characterize drought. This approach provides probability for each drought category instead of selecting a single final category at each epoch. We have compared PSDI with the deterministic approach (SDI) over major global basins. Our results show that the deterministic approach often leans towards an overestimation of storage-based drought severity. Furthermore, we scrutinize the performance of PSDI across diverse hydrologic events, spanning continents from the United States to Europe, the Middle East, Southern Africa, South America, and Australia. In each case, PSDI emerges as a reliable indicator for characterizing drought conditions, providing a more comprehensive perspective than traditional deterministic indices. In contrast to the common deterministic view, our probabilistic approach provides a more realistic characterization of the TWS drought, making it more suited for adaptive strategies and realistic risk management.

A document by Poulomi Ganguli. Click on the document to view its contents.

A document by Poulomi Ganguli. Click on the document to view its contents.

A document by Poulomi Ganguli. Click on the document to view its contents.

A document by Saman Aryana. Click on the document to view its contents.

A document by Saman Aryana. Click on the document to view its contents.

Current and emerging approaches to subsurface storage suffer from geographical limitations and in some cases insufficient seal integrity or lateral containment. We propose an innovative containment strategy based on time-dependent yield stress materials, namely Smectite clay suspensions, to address these challenges and make subsurface storage reliable and geographically agnostic. We outline a containment strategy designed to reinforce natural subsurface seals and engineer flow barriers. As a high-risk, high-reward approach, suspension can be injected at its initial low viscosity and elasticity into a porous medium, allowing for easy pumping and targeted delivery, once inside the target zone, it matures into a soft solid with much higher viscosity and elasticity, acting as a flow barrier. We introduce and discuss the exceptional properties of this Smectite suspensions that we believe can revolutionize subsurface containment and storage to move in tandem with the energy transition. There are no adverse effects from higher temperatures on its long-term stability, unlike most polymer aqueous dispersions used in the industry. Moreover, its thixotropic microstructure offers many advantages in operations, such as handling sudden pump shutdowns.

CO2-foams show significant potential for improving mobility control in CO2 Enhanced Oil Recovery (EOR) processes and for the geological carbon sequestration. The addition of nanoparticles (NPs), such as Fe3O4, has been shown to enhance the stability of these foams, especially when used in conjunction with surfactants. Apart from their role in stabilization, Fe3O4 NPs possess distinctive magnetic properties, making them useful in various applications. In this study, we explore the novel application of these nanoparticles, initially intended for stabilizing CO2-foams, as tracers for monitoring the migration of subsurface CO2 plumes. This monitoring is envisioned through the detection of small quantities of these particles within subsurface fluid environments. To facilitate this, we use an Induction Heating (IH) technique, involving exposing a solution with small amounts of Fe3O4 NPs to a high-frequency alternating magnetic field, then measuring the resulting temperature changes using an infrared camera variations. The results indicate a direct correlation between the NP concentration and the observed temperature increase in the solution.

A document by John Louie. Click on the document to view its contents.

A document by Matthew G Heberger. Click on the document to view its contents.

A document by Collin Bode. Click on the document to view its contents.

Porosity and permeability plays an important role in the investigation of the flow ability of unconventional rock. Unconventional rock such as shale gas has extremely low permeability due to nano-scale pores. Non-Darcy flow typically applies to any inertial flow where the Reynolds number is higher than 1. The study of non-Darcy flow in porous rocks by experiments carried out in the laboratory is consistently characterized by high costs and time-consuming procedures. In this study, a novel method called the lattice Boltzmann method, an alternative to the laboratory method has been used for the study of non-Darcy flow of shale gas. It provides matrix permeability from pore structure considering inertial flow which causes departure from Darcy’s law. The characteristics of non-Darcy flow are significantly influenced by the pore structure of a porous medium, with a more heterogeneous structure such as in the case of shale exhibiting a more rapid termination of Darcy flow. Two samples of shale as large as 5 mm of southern Karanpura were taken for this study. Computed micro-tomography images were acquired at 0.4 μm and 0.8 μm. The study of non-Darcy fluid flow in shale gas showed that as velocity increases, an inertial effect gets dominated in flow which results in a lowering of permeability. Earlier onset of non-Darcy behavior in complex structures is also investigated. Developing a complete understanding of the transport properties and developing an approach to assess the prospective gas flow is essential in informing the estimation of shale gas reserves and developing effective recovery strategies.

A document by Tolulope O. Odunola. Click on the document to view its contents.

Critical Zone (CZ) scientists study the coupled chemical, biological, physical, and geological processes operating across scales to support life at the Earth's surface. In 2020, the U.S. National Science Foundation funded a network of Thematic Cluster projects called “CZ Net” to work collaboratively in answering scientific questions related to effects of urbanization on CZ processes; CZ function in semi-arid landscapes and the role of dust in sustaining these ecosystems; deep bedrock processes and their relationship to CZ evolution; CZ recovery from disturbances such as fire and flooding; and changes in the coastal CZ related to rising sea level. Data collected by these projects are diverse, ranging from time series from in situ sensors to laboratory analysis of physical samples, geophysical measurements, and others. Thus, coordinating data collection, archival, discovery, and access for the network presents significant challenges. Given the diversity in scientific domains represented, data produced, and collaborations, no single repository fully meets the needs of CZ scientists, posing questions of which repositories to use, how to enable discovery of and access to data across different repositories, and how to develop and promote best practices for sharing research products. This presentation describes cyberinfrastructure (CI) development by the CZ Net Coordinating Hub that leverages existing, domain-specific repositories for managing, curating, disseminating, and preserving data and research products from the CZ Net projects. We have developed CI that links existing data facilities and services, including HydroShare, EarthChem, Zenodo, and other repositories via a CZ Hub that provides tools for data submission, resource registry, metadata cataloging, resource discovery/access, and links to computational resources for analysis and visualization. The CZ Hub’s goal is to make data, samples, software, and other research products created by CZ Net projects Findable, Accessible, Interoperable, and Reusable (FAIR), using existing domain-specific repositories. The repository interoperability we have demonstrated for delivering data services for an interdisciplinary science program may provide a template for future development of integrated, interdisciplinary data services.

Rapid warming in the Arctic leads to increased glacier melt and freshwater runoff, especially from tidewater glaciers. Here, runoff enters the fjord at depth; induces upwelling and enhances macronutrient delivery to the fjords. However, most studies have low temporal resolutions and so the effects of low-frequency, high-amplitude events on the marine environment remain poorly known. Here, we combine glacier observations with fjord and glacier lake sampling to describe the impact of the 2021 glacier lake outburst flood (GLOF) from lake Setevatnet into Kongsfjorden (Svalbard). We demonstrate the importance of changing subglacial conditions and examine their effects upon macronutrient availability in the inner fjord. Our observations reveal that direct nutrient subsidy from the glacier is most important in early summer, providing critical nitrate (NO3-) and silicate following the routing of meltwater through an inefficient drainage system. Increasing quantities of ice melt force the establishment of an efficient drainage system, creating a plume in the inner fjord, and resulting in upwelling of nutrient-rich bottom water. When the sudden drainage of a glacier lake with high NO3- concentrations occurred, it left little imprint on the NO3- content of the inner fjord, and instead induced seasonal maximum nitrite (NO2-) concentrations. This outcome implies that NO3- was removed by denitrification at the glacier bed and its product NO2- was discharged by the flood waters into the inner fjord. Our findings show that the delivery of key, productivity-limiting nutrients from tidewater glaciers not only depends on runoff, but also on characteristics of the glacier drainage system.

Seawater intrusion poses a substantial threat to water security in coastal regions, where numerical models play a pivotal role in supporting groundwater management and protection. However, the inherent heterogeneity of coastal aquifers introduces significant uncertainties into model predictions, potentially diminishing their effectiveness in management decisions. Data assimilation (DA) offers a solution by incorporating various types of observational data to characterize these heterogeneous coastal aquifers. Traditional DA techniques, like ensemble smoother using the Kalman formula (ESK) and Markov chain Monte Carlo, face challenges when confronted with the non-linearity, non-Gaussianity, and high-dimensionality issues commonly encountered in aquifer characterization. In this study, we introduce a novel DA approach rooted in deep learning (DL), referred to as ESDL, aimed at effectively characterizing coastal aquifers with varying levels of heterogeneity. We systematically investigate a range of factors that impact the performance of ESDL, including the number and types of observations, the degree of aquifer heterogeneity, the structure and training options of the DL models, etc. Our findings reveal that ESDL excels in characterizing heterogeneous aquifers, particularly when faced with non-Gaussian conditions. Comparison between ESDL and ESK under different experimentation settings underscores the robustness of ESDL. Conversely, in certain scenarios, ESK displays noticeable biases in the characterizing results, especially when measurement data from nonlinear and discontinuous processes are used. To optimize the efficacy of ESDL, meticulous attention must be given to the design of the DL model and the selection of training options, which are crucial to ensure the universal applicability of this DA method.

A document by Javed Ali. Click on the document to view its contents.

A document by Collin Bode. Click on the document to view its contents.