We use seismic ambient noise recorded by dense ocean bottom nodes (OBNs) in the Gorgon gas field, Western Australia, to compute time-lapse seafloor models of shear-wave velocity. The extracted hourly cross-correlation (CC) functions in the frequency band 0.1 – 1 Hz contain mainly Scholte waves with very high signal to noise ratio. We observe temporal velocity variations (dv/v) at the order of 0.1% with a peak velocity change of 0.8% averaged from all station pairs, from the conventional time-lapse analysis with the assumption of a spatially homogeneous dv/v. With a high-resolution reference (baseline) model from full waveform inversion of Scholte waves, we present an elastic wave equation based double-difference inversion (EW-DD) method, using arrival time differences between the reference and time-lapsed Scholte waves, for mapping temporally varying dv/v in the heterogeneous subsurface. The time-lapse velocity models reveal increasing/decreasing patterns of shear-wave velocity in agreement with those from the conventional analysis. The velocity variation exhibits a ~24-hour cycling pattern, which appears to be inversely correlated with sea level height, possibly associated with dilatant effects for porous, low-velocity shallow seafloor and rising pore pressure with higher sea level. This study demonstrates the feasibility of using dense passive seismic surveys for quantitative monitoring of subsurface property changes in the horizontal and depth domain.

Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the diagnosis of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest feature importance to detect the input variables that most influence simulated hydrological processes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX and PCR-GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoffs. The machine learning model could reproduce GHM/LSMs outputs with a coefficient of determination over 0.85 in all cases and often considerably better. The GHM/LSMs agreed precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regards to the relative importance of soil texture and topographic slope.

Water level drawdowns are increasingly common in lakes and reservoirs worldwide as a result of both climate change and water management. Drawdowns can have direct effects on physical properties of a waterbody (e.g., by altering stratification and light dynamics), and can also have emergent effects on the waterbody’s biology and chemistry. However, the emergent effects of drawdown remain poorly characterized in small, thermally-stratified reservoirs, which are common in the landscape. Here, we intensively monitored a small eutrophic reservoir for two years, including before, during, and after a month-long drawdown that reduced total reservoir volume by 36%. Our study aimed to quantify the effects of water level change on reservoir physical, chemical, and biological properties. During drawdown, stratification strength (maximum buoyancy frequency) and surface phosphate concentrations both increased, contributing to a substantial surface phytoplankton bloom. The peak in phytoplankton biomass was followed by cascading changes in surface water chemistry, with sequential peaks in dissolved organic carbon, dissolved carbon dioxide, and ammonium concentrations that reflect biogeochemical processes associated with bloom degradation. Dissolved oxygen concentrations substantially decreased in the surface waters during drawdown (to 41% saturation), which was associated with increased iron and manganese concentrations. Combined, our results illustrate how changes in water level can have emergent effects on coupled physical, chemical, and biological processes. As climate change and water management continue to increase the frequency of drawdowns in lakes worldwide, our results highlight the importance of characterizing how water level variability can alter complex in-lake ecosystem processes, thereby affecting water quality.

A document by Hongxiang Yan. Click on the document to view its contents.

A document by Sergio León-Ríos. Click on the document to view its contents.

We reprocessed and interpreted Chang’E-4 Lunar Penetrating Radar (LPR) data collected until 14th February 2023, exploiting a new Deep Learning-based algorithm to automatically extract reflectors from a processed radar dataset. The results are in terms of horizon probability and have been interpreted by integrating signal attribute analysis with orbital imagery. The approach provides more objective results by minimizing the subjectivity of data interpretation allowing to link radar reflectors to their geological context and surface structures. For the first time, we imaged dipping layers and at least 20 shallow buried crateriform structures within the regolith using LPR data. We further recognized four deeper structures similar to craters, locating ejecta deposits related to a crater rim crossed by the rover path and visible in satellite image data.

Climate risk management relies on accurate predictions of key climate variations such as El Niño-Southern Oscillation (ENSO), but the skill of ENSO predictions has recently plateaued or even degraded. Here we analyze the North American Multi-Model Ensemble (NMME) and estimate how the seasonal prediction of ENSO may benefit from reducing initial prediction errors. An analysis of predictable signals and system noises identifies a high-predictability regime and a low-predictability regime. The latter corresponds to the spring predictability barrier and is related to a rapid drop in the signal-to-noise ratio, which is caused by the comparably strong dampening of predictable signals. Reducing first-month prediction errors (FPEs) will likely reduce root-mean-square errors of the ENSO prediction. As a conservative estimate, halving the FPEs may extend the NMME’s skill by one to two months. Importantly, this study identifies the regions where reducing FPE is the most effective. Unlike the predictions initialized after the boreal spring, the March-initialized predictions of the wintertime ENSO will likely benefit the most from FPE reductions in the tropical Northwest Pacific. An opportunistic thought experiment suggests the buoy observation changes during 1995–2020 may have contributed to FPEs associated with large cold biases (>1K) in some El Niño-year predictions. While data availability prevented in-depth analyses of physical processes, the findings suggest that prioritizing modeling and observation in certain regions can improve climate predictions cost-effectively. The analytical framework here is applicable to other climate processes, thus holding wide potential for benefiting climate predictions.

Our understanding of tree stem methane (CH4) emissions is evolving rapidly. Few studies have combined seasonal measurements of soil, water and tree stem CH4 emissions from forested wetlands, inhibiting our capacity to constrain the tree stem CH4 flux contribution to total wetland CH4 flux. Here we present annual data from a subtropical freshwater Melaleuca quinquenervia wetland forest, spanning an elevational topo-gradient (Lower, Transitional and Upper zones). Eight field-campaigns captured an annual hydrological flood-dry-flood cycle, measuring stem fluxes on 30 trees, from four stem heights, and up to 30 adjacent soil or water CH4 fluxes per campaign. Tree stem CH4 fluxes ranged several orders of magnitude between hydrological seasons and topo-gradient zones, spanning from small CH4 uptake to ~203 mmol m-2 d-1. Soil CH4 fluxes were similarly dynamic and shifted from maximal CH4 emission (saturated soil) to uptake (dry soil). In Lower and Transitional zones respectively, tree stem CH4 contribution to the net ecosystem flux was greatest during flooded conditions (49.9 and 70.2 %) but less important during dry periods (3.1 and 28.2 %). Minor tree stem emissions from the Upper elevation zone still offset the Upper zone CH4 soil sink capacity by ~51% during dry conditions. Water table height was the strongest driver of tree stem CH4 fluxes, however tree emissions peaked once the soil was inundated and did not increase with further water depth. This study highlights the importance of quantifying the wetland tree stem CH4 emissions pathway as an important and seasonally oscillating component of wetland CH4 budgets.

The Sudan-Sahel region has long been vulnerable to environmental change. However, the intensification of global warming has led to unprecedented challenges that require a detailed understanding of climate change for this region. This study analyzes the impacts of climate change for Burkina Faso using eleven climate indices that are highly relevant to Sudan-Sahelian societies. The full ensemble of statistically downscaled NEX-GDDP-CMIP6 models (25 km) is used to determine the projected changes for the near (2031-2060) and far future (2071-2100) compared to the reference period (1985-2014) for different SSPs. Validation of the climate models against state-of-the-art reference data (CHIRPS and ERA5) shows reasonable performance for the main climate variables with some biases. Under the SSP5-8.5, Burkina Faso is projected to experience a substantial temperature increase of more than 4.3°C by the end of the century. Rainfall amount is projected to increase by 30% under the SSP5-8.5, with the rainy season starting earlier and lasting longer. This could increase water availability for rainfed agriculture but is offset by a 20% increase in evapotranspiration. The country could be at increased risk of flooding and heavy rainfall in all SSPs and future periods. Due to the pronounced temperature increase, heat stress, discomfort, and cooling degree days are expected to strongly increase under the SSP8.5 scenarios, especially in the western and northern parts. Under the SSP1-2.6 and SSP5-8.5, the projected changes are much lower for the country. Thus, timely implementation of climate change mitigation measures can significantly reduce climate change impacts for this vulnerable region.

Saturated hydraulic conductivity (Ks) is a crucial parameter that influences water flow in saturated soils, with applications in various fields such as surface water runoff, soil erosion, drainage, and solute transport. However, accurate estimation of Ks is challenging due to temporal and spatial uncertainties. This study addresses the knowledge gap regarding the long-term behaviour of Ks in sandy soils with less than 10% fine particles. The research investigates the changes in Ks over a long period of constant head tests and examines the factors influencing its variation. Two sandy samples were tested using a hydraulic conductivity cell, and the hydraulic head and discharge were recorded for over 50 days. The results show a general decline in Ks throughout the test, except for brief periods of increase. Furthermore, the relationship between flow rate and hydraulic head gradient does not follow the expected linear correlation from Darcy’s law, highlighting the complex nature of sandy soil hydraulic conductivity. The investigation of soil properties in three different sections of the samples before and after the tests revealed a decrease in the percentage of fine particles and a shift in specific gravity from the bottom to the top of the sample, suggesting particle migration along the flow direction. Factors such as clogging by fine particles and pore pressure variation contribute to the changes in Ks. The implications of this study have far-reaching effects on various geotechnical engineering applications. These include groundwater remediation, geotechnical stability analysis, and drainage system design.

We found a persistent increase in landward tidal attenuation in the Barataria region, indicating an increased risk of tidal intrusion over time. We inferred significant land subsidence rate changes with historically mapped land areas. By 2045, high tides will increase Barataria's land inundation, widening the land area gap between MSL and MHHW. Abstract This study investigates land loss and coastal inundation in Louisiana's Barataria Basin, a region highly susceptible to anthropogenic pressures and natural factors like land subsidence, sea-level rise, and tidal dynamics. Using high-resolution Digital Elevation Models (DEM) and water level data from the Coastal Reference Monitoring System (CRMS) stations, we analyzed changes in land area and water levels between 2007 and 2022. The attenuation coefficient magnitude of tidal intrusion, which quantifies tidal amplitude reduction as a function of landward distance from the coastline, exhibited a persistent decrease from 2007 to 2022 for O1 and K1 (the dominant tidal constituents), with an accumulated decrease of nearly 20%, signaling enhanced hydrological connectivity across the region. We also projected land area for historic years and predicted it for future years up to 2075, based on a range of displacement rates to account for uncertainties in vertical land motion. Our analyses predict that, in the absence of human intervention, the significance of tidal variations in influencing land loss will escalate; by 2045, the land area estimated based on Mean Higher High Water (MHHW) will constitute approximately 65% of the land area estimated using Mean Sea Level (MSL). Our findings underline the importance of considering the compound effects of subsidence, sea-level rise, and tidal dynamics in future land loss mapping and flood risk assessments. 

Diagnosing and predicting evaporation through satellite-based surface energy balance (SEB) and land surface models (LSMs) is challenging due to the non-linear responses of aerodynamic (ga) and stomatal conductance (gcs) to the coalition of soil and atmospheric drought. Despite a soaring popularity in refining gcs formulation in the LSMs by introducing a link between soil-plant hydraulics and gcs, the utility of gcs has been surprisingly overlooked in SEB models due to the overriding emphasis on eliminating ga uncertainties and the lack of coordination between these two different modeling communities. Therefore, a persistent challenge is to understand the reasons for divergent evaporation estimates from different models during strong soil-atmospheric drought. Here we present a virtual reality experiment over two contrasting European forest sites to understand the apparent sensitivity of the two critical conductances and evaporative fluxes to a water-stress factor (b-factor) in conjunction with land surface temperature (soil drought proxy) and vapor pressure deficit (atmospheric drought proxy) by using a non-parametric diagnostic model (Surface Temperature Initiated Closure, STIC1.2) and a prognostic model (Community Land Model, CLM5.0). Results revealed the b-factor and different functional forms of the two conductances to be a significant predictor of divergent response of the conductances to soil and atmospheric drought, which subsequently propagated in the evaporative flux estimates between STIC1.2 and CLM5.0. This analysis reaffirms the need for consensus on theory and models that capture the sensitivity of the biophysical conductances to the complex coalition of soil and atmospheric drought for better evaporation prediction.

To estimate groundwater flow and transport, lumped conceptual models are widely used due to their simplicity and parsimony - but these models are calibration reliant as their parameters are unquantifiable through measurements. To eliminate this inconvenience, we tried to express these conceptual parameters in terms of hydrodynamic aquifer properties to give lumped models a forward modelling potential. The most generic form of a lumped model representing groundwater is a unit consisting of a linear reservoir connected to a dead storage aiding extra dilution, or a combination of several such units mixing in calibrated fractions. We used one such standard two-store model as our test model, which was previously nicely calibrated on the groundwater flow and transport behaviour of a French agricultural catchment. Then using a standard finite element code, we generated synthetic Dupuit-Forchheimer box aquifers and calibrated their hydrodynamic parameters to exactly match the test model’s behaviour (concentration, age etc). The optimized aquifer parameters were then compared with conceptual parameters to find clear physical equivalence and mathematical correlation - we observed that the recession behaviour depends on the conductivity, fillable porosity, and length of the catchment whereas the mixing behaviour depends on the total porosity and mean aquifer thickness. We also noticed that for a two-store lumped model, faster and slower store represents differences only in porosities making it rather a dual porosity system. We ended with outlining a clear technique on using lumped models to run forward simulations in ungauged catchments where valid measurements of hydrodynamic parameters are available.

Fires were historically rare in tropical forests of West and Central Africa, where dense vegetation, rapid decomposition, and high moisture limit available fuels. However, increasing heat and drought combined with forest degradation and fragmentation are making these areas more susceptible to wildfire. We evaluated historical patterns of MODIS active fires in African tropical forests from 2003-2021. Trends were mostly positive, particularly in the northeastern and southern Congo Basin, and were concentrated in areas with high deforestation. Year-to-year variation of fires was synchronized with increasing temperature and vapor pressure deficit. There was anomalously high fire activity across the region during the 2015-2016 El Niño. These results contrast sharply with the drier African woodlands and savannas, where fires have been steadily decreasing. Further attention to fires in African tropical forests is needed to understand their global impacts on carbon storage and their local implications for biodiversity and human livelihoods.

A major advance in global bathymetric observation occurred in 2018 with the launch of NASA’s ICESat-2 satellite, carrying a green-wavelength, photon-counting lidar, the Advanced Topographic Laser Altimeter System (ATLAS). Although bathymetric measurement was not initially a design goal for the mission, pre- and post-launch studies revealed ATLAS’s notable bathymetric mapping capability. ICESat-2 bathymetry has been used to support a wide range of coastal and nearshore science objectives. However, analysis of ICESat-2 bathymetry in numerous locations around the world revealed instances of missing or clipped bathymetry in areas where bathymetric measurement should be feasible. These missing data were due to the ATLAS receiver algorithms not being optimized for bathymetry capture. To address this, two updates have been made to ICESat-2’s receiver algorithm parameters with the goal of increasing the area for which ICESat-2 can provide bathymetry. This paper details the parameter changes and presents the results of a two-phased study designed to investigate ICESat-2’s bathymetry enhancements at both local and global scales. The results of both phases confirm that the new parameters achieved the intended goal of increasing the amount of bathymetry provided by ICESat-2. The site-specific phase demonstrates the ability to fill critical bathymetric data gaps in open ocean and coastal settings. The global analysis shows that the area of potential bathymetry approximately doubled, with 6.1 million km2 of new area in which bathymetric measurements may be feasible. These enhancements are anticipated to facilitate a range of science objectives and close the gap between ICESat-2 bathymetry and offshore sonar data.

Redox processes, aqueous and solid-phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling in terrestrial and wetland ecosystems but are typically not included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox-sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, and methanogenesis as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.

A document by Aryav Bhesania. Click on the document to view its contents.

The spring phytoplankton bloom plays a major role in pelagic ecosystems; however, its dynamics is overlooked due to insufficient, highly-resolved observational data. Here we investigate the start, peak and decline of a two-week phytoplankton spring bloom in Frohavet, located at the coast of mid-Norway. We used observations from an uncrewed surface vehicle (USV) combined with buoy measurements, satellite images, discrete water sampling and modelling approaches. The spring bloom (March-June 2022) consisted of multiple peaks (up to 5 mg m-3), with a long peak in April, coincident with the period when the USV captured the temporal and spatial dynamics of the bloom. Short-term (5 days) episode of calm weather in the spring, such as clear skies and consistent low wind speed (< 7 m s-1) shoaled the mixed layer depth (< 15 m), after strong wind speed (average wind speed up to 20 m s-1 in March) and mixing events in winter. These rapid changes in the environment promoted the rapid development of the spring bloom - from 1 to 5 mg m-3 in 5 days. Likewise, the collapse of the bloom was rather quick, 1-2 days and coincides with low nitrate values and rapid increase in wind speed (> 10 m s-1), suggesting strong influence of the environment on phytoplankton dynamics during early stages of the spring bloom. Understanding the dynamics of the spring bloom is crucial for the management of marine resources. Integration of distinct observational platforms has the potential to unveil the environmental factors underlying phytoplankton bloom dynamics.

Large reductions in anthropogenic emissions of particulate matter and its precursor emissions have occurred since the enactment of the Clean Air Act Amendments of 1990. The Interagency Monitoring of Protected Visual Environments (IMPROVE) network has measured PM2.5 gravimetric mass (mass of particles with aerodynamic diameters less than 2.5 µm, also referred to here as fine mass, “FM”) and speciated PM2.5 aerosol composition at remote sites since 1988. Measured species include inorganic anions such as sulfate, nitrate, and chloride, carbonaceous aerosols such as organic (OC) and elemental carbon (EC), and elemental concentrations used to derive fine dust (FD). Trend analyses of seasonal and annual mean mass concentrations were calculated from 2000 through 2021, a period that includes the largest reductions in emissions. On average, annual mean FM at remote sites in the continental United States has decreased at a rate of -1.8% yr-1. This reduction is largely due to annual mean trends in sulfate (-6.1% yr-1), nitrate (-2.7% yr-1), EC (-2.2% yr-1), FD (-1.3% yr-1), and OC (-0.9% yr-1), although the OC annual mean trend was insignificant. Seasonal and regional FM trends varied significantly, with strong reductions in the East in all seasons due to sulfate reductions, and flat and insignificant trends in summer and fall in the West due to the impacts of biomass burning emissions on OC trends. Evaluating regional and seasonal trends in aerosol composition helps identify sources that continue to adversely impact air quality and hinder progress in FM reductions due to successful regulatory activity.

Co-located pressure and velocity observations in 10-15m depth are used to estimate the relative contribution of bound and free infragravity (IG) wave energy to the IG wave field. Shoreward and seaward going IG waves are analyzed separately. At the Southern California sites, shoreward propagating IG waves are dominated by free waves, with the bound wave energy fraction <30% for moderate energy incident sea-swell and <10% for low energy incident sea-swell. Only the 5% of records with energetic long swell show primarily bound waves. Consistent with bound IG wave theory, the energy scales as the square (frequency integrated) sea-swell energy, with a higher correlation with swell than sea energy. Seaward and shoreward free IG energy is strongly tidally modulated. The ratio of free seaward to shoreward propagating IG energy suggests between 50-100% of the energy radiated offshore is trapped on the shelf seaward of 10-15m and redirected shoreward. Remote sources of IG energy are small. The observed linear dependency of free seaward and shoreward IG energy on local sea-swell wave energy and tide are parameterized with good skill (R2 ~ 0.90). Free (random phase) and bound (phase-coupled) IG waves are included in numerically simulated timeseries for shoreward IG waves that are used to initialize (~ 10m depth) the numerical nonlinear wave transformation SWASH. On the low slope study beach, wave runup is only weakly influenced by free shoreward propagating waves observed at the offshore boundary (foreshore slope = 0.02).

Preprint, August 28th 2023Authors: Paolo Scussolini1*, Linh N. Luu2,3,4, Sjoukje Y. Philip4, Wouter R. Berghuijs5, Dirk Eilander1,6, Jeroen C.J.H. Aerts1, Sarah F. Kew4, Geert Jan van Oldenborgh4† , Willem H.J. Toonen5, Jan Volkholz7, Dim Coumou1,41Institute for Environmental Studies, Vrije Universiteit Amsterdam, Netherlands2Department of Geography, University of Lincoln, United Kingdom3Vietnam Institute of Meteorology, Hydrology and Climate Change, Hanoi, Vietnam4Royal Netherlands Meteorological Institute, Netherlands5Department of Earth Science, Vrije Universiteit Amsterdam, Netherlands6Deltares, Netherlands7Potsdam Institute for Climate Impact Research, Germany*Corresponding author: p.scussolini@vu.nl† Deceased, October 12th2021

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.

Multiphase flow through fractures has great significance in subsurface energy recovery and gas storage applications. Different fracture and flow properties affect flow through a fracture which is difficult to control in laboratory experiments. Here, we perform lattice Boltzmann simulations in an ensemble of synthetically generated fractures. Drainage simulations are performed at different capillary numbers, wettability, and viscosity ratios. We track the invading front and quantify breakthrough saturations and show that roughness and wettability have a profound effect on fluid invasion through a complex fracture. Invading a more viscous fluid results in more stable displacement regardless of the capillary number while at very low capillary numbers, fluid migration is dependent on the inherent structure of the fracture. Through a systematic investigation, we develop fluid displacement phase diagrams across multiple fractures, and demonstrate the importance of natural fracture features of roughness and wettability in establishing stable versus unstable displacement during multiphase flow.

Authors and affiliationsChristopher Masafu1, Richard Williams1, Martin D. Hurst11School of Geographical and Earth Sciences, University of Glasgow, Glasgow, G12 8QQ, UKCorresponding Author: Christopher Masafu (christopher.masafu@glasgow.ac.uk)