The UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC) is a new data product designed to address the challenges of monitoring inundation in regions hindered by dense vegetation and cloud cover as is the case in most of the Tropics. The Cyclone Global Navigation Satellite System (CYGNSS) constellation provides data with a higher temporal repeat frequency compared to single-satellite systems, offering the potential for generating moderate spatial resolution inundation maps with improved temporal resolution while having the capability to penetrate clouds and vegetation. This paper details the development of a computer vision algorithm for inundation mapping over the entire CYGNSS domain (37.4°N to 37.4°S). The unique reliance on CYGNSS data sets our method apart in the field, highlighting CYGNSS’s indication of water existence. Berkeley-RWAWC provides monthly, near-real-time inundation maps starting in August 2018 and across the CYGNSS latitude range, with a spatial resolution of 0.01° × 0.01°. Here we present our workflow and parameterization strategy, alongside a comparative analysis with established surface water datasets (SWAMPS, WAD2M) in four regions: the Amazon Basin, the Pantanal, the Sudd, and the Indo-Gangetic Plain. The comparisons reveal Berkeley-RWAWC’s enhanced capability to detect seasonal variations, demonstrating its usefulness in studying tropical wetland hydrology. We also discuss potential sources of uncertainty and reasons for variations in inundation retrievals. Berkeley-RWAWC represents a valuable addition to environmental science, offering new insights into tropical wetland dynamics.

Flash droughts– the rapid drying of land and intensification of drought conditions - have devasting impacts to natural resources, food supplies, and the economy. Less is currently known about the drivers of flash droughts and their impact to landscape carbon losses. We leverage carbon and water cycle data from NASA OCO-2 and SMAP missions to determine the net impact of flash drought events in the U.S. on the carbon sinks. On average, pre-onset carbon uptake fully offsets post-onset losses, creating a carbon neutral biosphere over a +/- 3 month period surrounding flash drought onset. This contrasts with ecosystem models, which underestimate pre-onset uptake and overestimate post-onset loss. Furthermore, spaceborne observations of solar induced fluorescence (SIF) provide a reliable indicator of flash droughts at lead times of 2-3 months, due to feedbacks between vegetation growth and soil water loss. This study is expected to improve understanding and prediction of flash droughts.

Dam breach is rare event in which dam fails releasing impounded water to downstream regions. Dam breach has low probability of occurrence but carries high risk of destruction. Dudhkoshi Storage Hydroelectric Project concrete faced rock fill dam (CFRD) was studied for dam breach under overtopping and piping failure modes. Dam breach simulation and flood propagation study is vital for identifying and minimizing the risks associated with breach flood. Two scenarios namely base-case scenario with average value of dam breach parameters and worst-case scenario with value of dam breach parameters resulting in maximum output. Local and global sensitivity analysis are performed for four dam breach parameters (dam breach width, breach formation time, weir coefficient, trigger failure elevation). Sensitivity analysis is performed for two river profile. Sensitivity on peak discharge, peak velocity, arrival time and water surface elevation were evaluated. ArcGIS, HEC-RAS and OriginPro 2022b are used for dam breach analysis. Overtopping failure was found to be critical as compared to piping mode.

A document by Daniel White. Click on the document to view its contents.

A document by Nicholas Christensen. Click on the document to view its contents.

Reactive Transport Models (RTMs) are essential for understanding and predicting intertwined ecohydrological and biogeochemical processes on land and in rivers. While traditional RTMs have focused primarily on subsurface processes, recent RTMs integrate hydrological and biogeochemical interactions between land surface and subsurface. These emergent, watershed-scale RTMs are often spatially explicit and require large amount of data and extensive computational expertise. There is however a pressing need to create parsimonious models that require less data and are accessible to scientists with less computational background. Here we introduce BioRT-HBV 1.0 (hereafter BioRT), a watershed-scale, hydro-biogeochemical model that builds upon the widely used, bucket-type HBV model (Hydrologiska Bryåns Vattenavdelning), known for its simplicity and minimal data requirements. BioRT uses the conceptual structure and hydrology output of HBV to simulate processes including solute transport and biogeochemical reactions driven by reaction thermodynamics and kinetics. These reactions include, for example, chemical weathering, soil respiration, and nutrient transformation. This paper presents the model structure and governing equations, demonstrates its utility with examples simulating carbon and nitrogen processes in a headwater catchment. As shown in the examples, when constrained by data, BioRT can be used to illuminate the dynamics of biogeochemical reactions in the invisible, arduous-to-measure subsurface, and their connections to observed solute export in streams and rivers. We posit that such parsimonious models increase model accessibility to users without in-depth computational training. It also can serve as an educational tool that promote pollination of ideas across different fields and foster a more diverse, equal, and inclusive user community.

Fire affects soil and vegetation, which in turn can promote the initiation and growth of runoff-generated debris flows in steep watersheds. Postfire hazard assessments often focus on identifying the most likely watersheds to produce debris flows, quantifying rainfall intensity-duration thresholds for debris flow initiation, and estimating the volume of potential debris flows. This work seeks to expand on such analyses and forecast downstream debris flow runout and peak flow depth. Here, we report on a high fidelity computational framework that enables debris flow simulation over two watersheds and the downstream alluvial fan, although at significant computational cost. We also develop a Gaussian Process surrogate model, allowing for rapid prediction of simulator outputs for untested scenarios. We utilize this framework to explore model sensitivity to rainfall intensity and sediment availability as well as parameters associated with saturated hydraulic conductivity, hydraulic roughness, grain size, and sediment entrainment. Simulation results are most sensitive to peak rainfall intensity and hydraulic roughness. We further use this approach to examine variations in debris flow inundation patterns at different stages of postfire recovery. Sensitivity analysis indicates that constraints on temporal changes in hydraulic roughness, saturated hydraulic conductivity, and grain size following fire would be particularly beneficial for forecasting debris flow runout throughout the postfire recovery period. The emulator methodology presented here also provides a means to compute the probability of a debris flow inundating a specific downstream region, consequent to a forecast or design rainstorm. This workflow could be employed in scenario-based planning for postfire hazard mitigation.

Fractures play important roles in fluid and heat flow during heat extraction from an enhanced geothermal system (EGS). Quantifying the associated uncertainties in fractures is critical for predicting long-term thermal performance of EGSs. Considerable advancements have been made regarding the inversion of fracture characteristics such as aperture distribution. However, most previous studies assumed a constant fracture aperture to simplify the inversion, while both experimental and numerical results indicated significant variations in fracture aperture due to complex thermo-hydro-mechanical (THM) coupled processes during heat extraction. This study introduces a multi-stage inversion framework that integrates the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) with a THM coupled model to capture the dynamic evolution of fracture aperture. The framework executes multiple aperture inversions at different times during EGS operation. In each inversion stage, we use ES-MDA to invert for fracture aperture by assimilating tracer data, and then perform THM modeling to analyze fracture aperture evolution under coupled THM processes and predict thermal performance. We propose a principle to assure a smooth transition between two consecutive inversion stages, that the posterior aperture fields obtained in an inversion stage are used as the prior aperture fields for the following stage, and the temperature field simulated in the previous inversion stage serves as the initial temperature field for the following stage. Application of the framework to a synthetic field-scale EGS model demonstrates its efficacy in capturing the dynamic evolution of fracture aperture, resulting in more accurate thermal predictions compared with previous inversion methods assuming constant fracture aperture.

A document by Christopher B Marsh. Click on the document to view its contents.

A document by Andy Baker. Click on the document to view its contents.

The Regional Climate Modeling system (RegCM) has undergone a significant evolution over the years, leading for example to the widely used versions RegCM4 and RegCM4-NH. In response to the demand for higher resolution, a new version of the system has been developed, RegCM5, incorporating the non-hydrostatic dynamical core of the MOLOCH weather prediction model. In this paper we assess the RegCM5’s performance for 5 CORDEX-CORE domains, including a pan-European domain at convection-permitting resolution. We find temperature biases generally in the range of -2 to 2 degrees Celsius, higher in the northernmost regions of North America and Asia during winter, linked to cloud water overestimation. Central Asia and the Tibetan Plateau show cold biases, possibly due to sparse station coverage. The model exhibits a prevailing cold bias in maximum temperature and warm bias in minimum temperature, associated with a systematic overestimation of lower-level cloud fraction, especially in winter. Taylor diagrams indicate a high spatial temperature pattern correlation with ERA5 and CRU data, except in South America and the Caribbean region. The precipitation evaluation shows an overestimation in South America, East Asia, and Africa. RegCM5 improves the daily precipitation distribution compared to RegCM4, particularly at high intensities. The analysis of wind fields confirms the model’s ability to simulate monsoon circulations. The assessment of tropical cyclone tracks highlights a strong sensitivity to the tracking algorithms, thus necessitating a careful model interpretation. Over the European region, the convection permitting simulations especially improve the diurnal cycle of precipitation and the hourly precipitation intensities.

During characterization efforts of complex sites and geologies, it is important to estimate material properties efficiently and robustly. We present data and modeling related to the heat of wetting process during spontaneous imbibition, as observed in zeolitic tuff. The heat of wetting is due to adsorption of liquid water and water vapor to an oven-dry core sample and results in an observable temperature rise. The fitting of numerical models to imbibition observations allows simultaneous constraint of single-phase (porosity, permeability), two-phase (van Genuchten m and alpha), thermal (thermal diffusivity), and transport (tortuosity) properties from a single imbibition test. Petrographic analysis informs how microstructure connectivity and pore-lining phases affect the imbibition process. Estimating multiple properties simultaneously from a single test on a core sample helps ensure consistency in interpreted material properties. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525 (SAND2023-07021A).

The East Asian monsoon system is generated by land-ocean thermal contrast between Asia and NW Pacific, altering the hydroclimate variability in the region. Suspended particulate matter (SPM) consists of a mixture of geochemical pools with distinct origins, evolutionary paths, behaviors and isotopic signatures. The characteristics of SPM reflect the physical processes occurring within a river catchment, including erosion, sediment transport, nutrient and pollutant movement, and organic matter dynamics. The Changjiang is the second largest supplier of global SPM, discharges the largest fresh water and suspended sediment discharge into the East China Sea, significantly influencing the oceanic mass balance. The Changjiang basin is subject to the Indian Monsoon in its headwaters and the East Asian Monsoon downstream, which dominates the chemical weathering patterns in the basin.A widespread inherited feature of the Changjiang basin is its heavy Cu signatures in dissolved and particulate loads. However, the SPM samples conversely displays light 𝛿65Cu values in Lower Reaches, ranging from −0.26 to +0.45 ‰ (2SD, n=31), with almost constant CuSPM concentrations (average ~49 µg/g). Heavy and light Cu isotopic signatures are naturally sourced, albeit with considerable fluctuations in daily samples. The flood event in July 2020 elucidate the effect of intense erosion over Cu concentrations and isotopes, showing larger variations from −0.10 to +0.45 ‰ (2SD, n=10). We infer that the temporal and spatial variations in Cu isotopes of Changjiang SPMs with low enrichment metrics is the combined effects of monsoon-driven weathering and soil erosion in the large catchment. This study provides new insights into Cu geochemical behavior during earth surface processes, which will contribute to a holistic understanding of the global Cu cycle.

Urban Heat Island (UHI) effects have significant implications on the microclimatic conditions in urban environments, impacting human health, energy consumption, and overall urban planning. This study aims to assess the diurnal intensity of UHI in a microclimatic urban setting by adopting the Local Climate Zone (LCZ) classification approach. We utilized a combination of remote sensing data, ground-based measurements, and LCZ classification to analyze the temporal and spatial variation of UHI intensity throughout the day and night. The study area, Dehradun city, a densely populated urban area situated in the valley region of Himalayas, exhibited diverse LCZs, including compact low-rise, dense trees, and open spaces. Using satellite-derived land surface temperature (LST) data and hourly in-situ measurements, we quantified the UHI effect during daytime and nighttime hours. The results revealed distinct diurnal patterns of UHI intensity among different LCZs, with peak intensity occurring during late afternoon and early evening hours. Furthermore, we investigated the impact of vegetation and built-up characteristics on UHI variation, highlighting the cooling effect of green spaces and the amplifying effect of impervious surfaces. This research contributes to a better understanding of microclimatic urban environments and their relation to UHI dynamics, providing valuable insights for urban planners, policymakers, and researchers aiming to mitigate heat-related issues and promote sustainable urban development. The findings underscore the importance of considering local land-use patterns and urban morphology when assessing and managing UHI effects.

An accurate spatial and temporal representation of rainfall is essential for hydrological assessments and water resources management. Rainfall is monitored in India’s mountainous Western Ghats region via in-situ rainfall gauging stations maintained by the Indian Meteorological Department (IMD). However, the network is sparse, and significant periods of data are missing. Furthermore, the IMD gridded rainfall dataset is known to underestimate the depth of rainfall at the high altitudes within this region. In this study, rainfall estimated by the IMD grids and from remote sensing using the CHIRPS (0.25- and 0.05- degree), MSWEP and PERSIANN datasets are compared to the IMD in-situ gauged rainfall within the Western Ghats using a point-to-pixel analysis. The GWAVA model is utilised to determine the effect of the selected rainfall input datasets on representing wider water resources. It was found that the average ensemble provided the best representation of the in-situ gauged and catchment rainfall and a better representation than the IMD grids. It remains critical for water resources management to ensure that in-situ rainfall gauging networks are maintained. In-situ data sources of high confidence remain important for the continuous development and ground-truthing of different rainfall datasets.

A document by Yuki Kimura. Click on the document to view its contents.

It is well-established that positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impacts land-atmosphere interactions, disrupts the global carbon cycle, and accelerates climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impact. Currently, few earth system models account for permafrost carbon feedback mechanisms. This research identifies, interprets, and explains the feedback sensitivities attributed to permafrost degradation and terrestrial carbon cycling imbalance with in-situ and flux tower measurements, remote sensing observations, process-based modeling simulations, and deep learning architecture. We defined and formulated high-resolution polymodal datasets with multitemporal extents and hyperspatiospectral fidelity (i.e., 12.4 million parameters with 13.1 million in situ data points, 2.84 billion ground-controlled remotely sensed data points, and 36.58 million model-based simulation outputs to computationally reflect the state space of the earth system), simulated the non-linear feedback mechanisms attributed to permafrost degradation and carbon cycle perturbation across Alaska with a process-constrained deep learning architecture composed of cascading stacks of convolutionally layered memory-encoded recurrent neural networks (i.e., GeoCryoAI), and interpreted historical and future emulations of freeze-thaw dynamics and the permafrost carbon feedback with a suite of evaluation and performance metrics (e.g., cross-entropic loss, root-mean-square deviation, accuracy). This framework introduces ecological memory components and effectively learns subtle spatiotemporal covariate complexities in high-latitude ecosystems by emulating permafrost degradation and carbon flux dynamics across Alaska with high precision and minimal loss (RMSE: 1.007cm, 0.694nmolCH4m-2s-1, 0.213µmolCO2m-2s-1). This methodology and findings offer significant insight about the permafrost carbon feedback by informing scientists and the public on how climate change is accelerating, strategies to ameliorate the impact of permafrost degradation on the global carbon cycle, and to what extent these connections matter in space and time.

It is well-established that positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impacts land-atmosphere interactions, disrupts the global carbon cycle, and accelerates climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impact. Currently, few earth system models account for permafrost carbon feedback mechanisms. This research identifies, interprets, and explains the feedback sensitivities attributed to permafrost degradation and terrestrial carbon cycling imbalance with in situ and flux tower measurements, remote sensing observations, process-based modeling simulations, and deep learning architecture. We defined and formulated high-resolution polymodal datasets with multitemporal extents and hyperspatiospectral fidelity (i.e., 12.4 million parameters with 13.1 million in situ data points, 2.84 billion ground-controlled remotely sensed data points, and 36.58 million model-based simulation outputs to computationally reflect the state space of the earth system), simulated the non-linear feedback mechanisms attributed to permafrost degradation and carbon cycle perturbation across Alaska with a process-constrained deep learning architecture composed of cascading stacks of convolutionally layered memory-encoded recurrent neural networks (i.e., GeoCryoAI), and interpreted historical and future emulations of freeze-thaw dynamics and the permafrost carbon feedback with a suite of evaluation and performance metrics (e.g., cross-entropic loss, root-mean-square deviation, accuracy). This framework introduces ecological memory components and effectively learns subtle spatiotemporal covariate complexities in high-latitude ecosystems by emulating permafrost degradation and carbon flux dynamics across Alaska with high precision and minimal loss (RMSE: 1.007cm, 0.694nmolCH4m-2s-1, 0.213µmolCO2m-2s-1). This methodology and findings offer significant insight about the permafrost carbon feedback by informing scientists and the public on how climate change is accelerating, strategies to ameliorate the impact of permafrost degradation on the global carbon cycle, and to what extent these connections matter in space and time.

Arctic sea ice is retreating, thinning, and exhibiting increased mobility. In the Beaufort Gyre (BG), liquid freshwater content (FWC) has increased by 40\% in the last two decades, with sea ice melting being a primary contributor. This study utilizes satellite observations of sea surface salinity (SSS) and sea ice concentration, along with model-based sea ice thickness from 2011 to 2019. The aim is to investigate the sea ice-SSS relationship at different scales in the Arctic and understand the sea-ice meltwater dynamics in the BG. Our findings reveal a strong synchrony and positive correlation between sea ice area and SSS in the Arctic Ocean. In September, when the BG exhibits the largest ice-free ocean surface, a noticeable release of freshwater from sea ice melting occurs, a phenomenon not accurately reproduced by the models. The SMOS (Soil Moisture and Ocean Salinity) mission proves valuable in detecting meltwater lenses (MWL) originating from sea ice melting. These MWLs exhibit mean SSS ranging from 19 psu at the begining of sea ice retreat to 25 psu before sea ice formation. Wind-driven anticyclonic eddies can trap MWLs, preserving the freshest SSS imprints on the sea surface for up to 10 days. Furthermore, events of sea surface salinification following sea ice formation suggest that SMOS SSS might be capturing information on brine rejection. The daily evolution of sea ice-SSS within the MWLs demonstrates a tight correlation between both variables after sea ice melting and just before sea ice formation, indicating a transient period in between.

Quantifying the extent of drought and flood magnitude and frequency under the climate change impacts is essential for an effective water resource management. In this study, we utilize the Soil and Water Assessment Tool (SWAT) hydrological model, drought indices as well as the Interquartile Range (IQR) method for a comprehensive analysis of the river flow response to projected climate change scenarios. Four General Circulation Models (GCMs) under two Shared Socioeconomic Pathways (SSP2-4.5 and 5-8.5) have been used for our analysis (2023-2090). Our objective is to reveal the future projected drought and flood events in terms of intensity, frequency, and potential consequences for local livelihoods in the Srepok River basin (SRB), a tributary of the Mekong River basin (MRB), Southeast Asia. Our findings serve as the scientific basis for stakeholders and decisionmakers to develop adaptative strategies and sustainable plans to promote the region's resilience.

The Pra Basin in Ghana is well-known for its abundant mineral resources, dense forest coverage, and fertile soil. The region faces major water management challenges due to illegal mining practices, resulting in surface water pollution and necessitating groundwater use as an alternate water source. Unfortunately, there is limited information available regarding the chemical characteristics of groundwater in the region, posing challenges for water management. This study examined the quality, hydrochemical variability and geochemical processes driving the chemical evolution of the groundwater. Samples of surface water and groundwater were collected and analyzed for chemical parameters. We employed multivariate statistics, including cluster and factor analysis, to identify regional variations and interrelationships among the parameters. The resulting clusters were used to formulate a hypothetical groundwater flow path to model the geochemical reactions that control the groundwater composition using combinatorial inverse modelling based on the local thermodynamic equilibrium hypothesis. The weathering of silicate minerals, including albite, anorthite, chalcedony, and k-feldspar, was found to be the dominant process driving the groundwater's chemical evolution. Models adequately predicted the composition of groundwater along the flow path and serve as a guide for the development of sustainable water resource management strategies for the catchment. Overall, our modelling approach presented here can be useful in regions with large variability in water chemistry and limited knowledge of aquifer mineralogy.

Mohamed Hamdy Eid a,b*,  Attila Kovácsa and Péter Szűcs aaInstitute of Environmental Management, Faculty of Earth Science, University of Miskolc, 3515, Hungary; [email protected]; [email protected]; [email protected]. bGeology Department, Faculty of Science, Beni-Suef University, Beni-Suef, 65211, Egypt* Corresponding author: Mohamed Hamdy Eid a,b*; [email protected] ORCID: 0000-0002-3383-1826Final Paper Number: H31U-1778  Presentation Type: Poster Session Number and Title: H31U: Frontiers in Water Quality I Poster Session Date and Time: Wednesday, December 13th; 8:30 AM - 12:50 PM PST Location: MC, Poster Hall A-C – South Abstract The current study evaluates the different factors threatening the sustainability of Siwa Oasis including soil salinization, water quality deterioration, water logging, depletion of non-rechargeable water resources and providing water management plan. GIS and remote sensing supported with machine learning were used for change detection in the land cover from 1990 to 2020. The hydrodynamic condition in the deep Nubian sandstone aquifer (NSSA) was investigated using pressure-depth pro le. The groundwater salinity was monitored from 1998 to 2022. Geochemical model using PHREEQC was conducted to detect the types of minerals that have the ability to precipitate in the soil from irrigation water and decrease its permeability. The change detection in the land cover showed rapid increase in the surface area of the salt lakes from 22.6 km2 in 1990 to 60.6 km2 in 2020. The soil salinization increased in the central Siwa Oasis due to evaporation of water logged in the soil. Monitoring the water salinity from 1998 to 2022 showed rapid deterioration in groundwater quality of the Tertiary carbonate aquifer (TCA). The pressure-depth pro le showed that the water in NSSA is over hydrostatic pressure in the eastern and western part of the study area and the central part is under hydrostatic pressure indicating pressure decrease. Chadha diagram and piper diagram showed that the water type changed upward from Ca-Mg-HCO3 in the rst stage in NSSA to Na-Cl type in the last stage in TCA and surface water. The saturation index revealed that the majority of water samples were supersaturated with respect to calcite, dolomite, talc, Ca-montmorillonite, chlorite, gibbsite, illite, K-mica, hematite, chrysotile and kaolinite, while the samples were undersaturated with halite, anhydrite, gypsum, and CO2. The irrigation water quality indices showed that NSSA is suitable for irrigation purposes while TCA is not suitable for irrigation regarding magnesium hazards (MH) and potential salinity (PS). The water quality regarding sodium adsorption ratio (SAR) and sodium percent (Na%) range from good to poor and good according to residual sodium carbonate (RSC). Application of subsurface drip irrigation, and mixing water of TCA and NSSA could be the best management of the water resources in Siwa Oasis. 

A document by Carolina Hurtado-Pulido. Click on the document to view its contents.

Rainfall plays a particularly decisive role in areas prone to flooding, where deviations in rainfall patterns can dramatically impact water availability, transportation systems, environmental health, and short term urban planning. The ability to accurately predict rainfall can greatly assist government bodies, and private entities, allowing them to strategize and make informed short-term decisions in areas such as disaster management and early hazard warning systems , especially during periods of flood. In this context, Artificial Intelligence (AI) is playing an increasingly significant role in enabling precise predictions of rainfall. The study at hand leverages AI-based models to forecast next-day rainfall, focusing particularly on regions susceptible to flooding. The dataset used includes ten years' worth of daily weather observations from multiple flood-prone locations in India. The AI model makes its predictions using a range of meteorological indicators such as minimum and maximum temperatures, rainfall, evaporation, sunshine, wind gust speed, wind speed, humidity, pressure, cloud cover, and temperatures at two intervals of the day (9 am and 3 pm). The machine learning model employed is a sequential model with four layers, which incorporates dropout for regularisation. The initial model, utilising an early stopping callback, achieved an accuracy of 90.85%. In a bid to further enhance this, a Reduce Learning Rate on Plateau callback and a custom accuracy printing callback were introduced, leading to a remarkable improvement in predictive accuracy, with the enhanced model achieving a score of 94.78%. This approach can bring substantial benefits across various sectors, such as transportation, environmental planning, evacuation and rescue work in flood areas, by equipping them with reliable rainfall predictions to base their decisions upon.