Water levels in lakes is a determinant of water availability for optimum water resources planning and ecosystem functioning. This study examines the surface water fluctuations in Kainji and Shiroro Lakes between 2008 and 2022 (14 years) using satellite altimetry. The purpose is to monitor the effect of precipitation, as an indicator of climatic variability on the water levels in the lakes considering their use for hydropower generation and flood control .From the observed values, there was no significant difference in water levels with the highest recorded at Kainji Lake as 138.84m and the lowest as 125.77m, however, there was significant difference in the  area coverage with the highest value as 1311.16km2  and lowest value as 761.15km2.Same trend was noticed for Shiroro Lake where the highest water level was 381.92m and lowest as 356.97m; there was a marked difference in area coverage with the highest area being 361.61km2 and lowest 104.36km2 . A further analysis was done to assess the spatiotemporal trend of the rainfall at the Kainji and Shiroro Lakes from rainfall data derived from TAMSAT. Using Mann Kendall (MK) and Spearman’s rho tests for the 14-year period, trend analysis for the Kainji Lake showed a slight positive trend for precipitation at a 5% significance level; with mean annual rainfall of 1137. 7mm.At the Shiroro Lake, there was no positive trend at a 5% significance level. The mean annual rainfall recorded was 1318.6m. Key Words: Precipitation, Significance level, Mann Kendall, Rainfall, Trend

The Tropical West Pacific (TWP) is the key entry point of air into the stratosphere during Northern Hemispheric winter. Our previous work showed how transport to the TWP troposphere is modulated by the movement of the Intertropical Convergence Zone, allowing transport of polluted air masses from tropical Asia during spring (Müller et al., 2023a, Müller et al. 2023b). Here, we present insights on air mass transport to the upper troposphere/lower stratosphere (UTLS) in this region using the ozone and relative humidity time series (2016-2023) from the Palau Atmospheric Observatory (PAO), located in the center of the tropical warm pool (7°N, 134°E). We further give an overview of all observations performed at the PAO during the ACCLIP summer campaign 2022, including ozone, CFH and COBALD sonde and lidar measurements.In analogy to Müller et al. 2023b, Lagrangian backward trajectories calculated are used to identify transport from remote locations. The time series already allows some interpretation regarding interannual variability mainly to the ENSO cycle. Here, we show anomalies during the 2016 strong El Nino and potentially the El Nino in the upcoming winter 2023. The PAO proves to be an excellent site to study UTLS composition and dynamics in the TWP region.

The geostationary Himawari-8 satellite offers a unique opportunity to monitor sub-daily thermal dynamics over Asia and Oceania, and several operational land surface temperature (LST) retrieval algorithms have been developed for this purpose. However, studies have reported inconsistency between LST data obtained from geostationary and polar-orbiting platforms, particularly for daytime LST, which usually shows directional artefacts and can be strongly impacted by viewing and illumination geometries and shadowing effects. To overcome this challenge, Solar Zenith Angle (SZA) serves as an ideal physical variable to quantify systematic differences between platforms. Here we presented an SZA-based Calibration (SZAC) method to operationally calibrate the daytime component of a split-window retrieved Himawari-8 LST (referred to here as the baseline). SZAC describes the spatial heterogeneity and magnitude of diurnal LST discrepancies from different products. The SZAC coefficient was spatiotemporally optimised against highest-quality assured (error < 1 K) pixels from the MODerate-resolution Imaging Spectroradiometer (MODIS) daytime LST between 01/Jan/2016 and 31/Dec/2020. We evaluated the calibrated LST data, referred to as the Australian National University LST with SZAC (ANUSZAC), against MODIS LST and the Visible Infrared Imaging Radiometer Suite (VIIRS) LST, as well as in-situ LST from the OzFlux network. Two peer Himawari-8 LST products from Chiba University and the Copernicus Global Land Service were also collected for comparisons. The median daytime bias of ANUSZAC LST against Terra-MODIS LST, Aqua-MODIS LST and VIIRS LST was 1.52 K, 0.98 K and -0.63 K, respectively, which demonstrated improved performance compared to baseline (5.37 K, 4.85 K and 3.02 K, respectively) and Chiba LST (3.71 K, 2.90 K and 1.07 K, respectively). All four Himawari-8 LST products showed comparable performance of unbiased root mean squared error (ubRMSE), ranging from 2.47 to 3.07 K, compared to LST from polar-orbiting platforms. In the evaluation against in-situ LST, the overall mean values of bias (ubRMSE) of baseline, Chiba, Copernicus and ANUSZAC LST during daytime were 4.23 K (3.74 K), 2.16 K (3.62 K), 1.73 K (3.31 K) and 1.41 K (3.24 K), respectively, based on 171,289 hourly samples from 20 OzFlux sites across Australia between 01/Jan/2016 and 31/Dec/2020. In summary, the SZAC method offers a promising approach to enhance the reliability of geostationary LST retrievals by incorporating the spatiotemporal characteristics observed by accurate polar-orbiting LST data. Furthermore, it is possible to extend SZAC for LST estimation by using data acquired by geostationary satellites in other regions, e.g., Europe, Africa and Americas, as this could improve our understanding of the error characteristics of overlapped geostationary imageries, allowing for targeted refinements and calibrations to further enhance applicability.

The increased occurrence of catastrophic events caused by climate change-induced mass movements affects human welfare and results in huge economic losses, making the development of early warning systems (EWSs) crucial to everyday decisions of local governments and communities. The state-of-the-art (SOTA) EWSs can only provide information on the level of direct danger of mass movement that each village is exposed to. This is the result of highly sensitive and coarse classifications and aggregated predictions at regional level, potentially leading to a poor perception of risk and inadequate local management plans. Also, this does not account for the indirect effects of mass movements on the local communities at social and economic levels, e.g., a town that can be cut out of the transportation and communication systems because of landslides blocking the roads around it is currently not considered by SOTA EWSs.To overcome this issue, we developed a novel machine learning scheme that investigates the environmental (hydrological and geological) characteristics of mass movements and the connectivity information of formal settlements and road network data in terms of graph structures. In particular, we study the interaction of the probabilistic mass movement susceptibility (derived from the environmental properties by means of a supervised ensemble graph neural network) on the graph representing the road network connecting the formal settlements. As a result, we derive for each formal settlement a probability of being indirectly affected by mass movements (e.g., the probability to be isolated as a result of mass movements affecting their surroundings) by graph spectral clustering.We tested this architecture (named Intergraph) on the Norwegian territory, taking advantage of over 68,000 incidents of reported mass movements since 1957. Our approach achieved an overall performance of 86.25% with the 2020 Gjerdrum quick clay incident as a demonstrated case study. With the intensifying effects of climate change, our study has opened an opportunity to develop solutions for adaptation and mitigation through a new holistic graphical perspective to assess various large-scale geospatial datasets of risk elements such as exposure, vulnerability, and hazard.

A document by Karlyn Harrod. Click on the document to view its contents.

Hydrological observation networks are inadequate and declining, hindering advancements in hydrology, particularly in hydrological budgeting, where interception loss remains unmeasured, and a subjective loss figure is assumed. Measuring net rainfall reaching the ground surface is crucial for precise water balance estimates. This involves measuring throughfall & stemflow fractions of precipitation on the canopy. Current methods use collector channels and static storage systems requiring periodic visits for measurements, which becomes extremely difficult and unsafe in inaccessible forests, leading to data losses. For time-resolved data, an array of standard rain gauges is placed under the canopy, or water is collected by troughs for throughfall (and collars for stemflow measurement), draining into a central pipe and directed into a tipping bucket flow gauge. Commercial flow gauges are expensive for use in throughfall & stemflow measurement, since, multiple instruments are required in each study location to capture the variability of the canopy. To address this, we designed and fabricated an open-source, low-cost Tipping Bucket Flow Gauge to automatically monitor flow rates of throughfall & stemflow. It is designed to have a larger adjustable tipping resolution (10 ml – 200 ml). The open-source Arduino-based data logger automatically collects time-resolved data and is powered using solar energy, ensuring remote functionality even in harsh environments. A modular electronics approach was followed for designing the datalogger, facilitating rapid prototyping, easy repair, and upgrades, enabling someone with even an introductory knowledge in Arduino to implement the design. Almost 75% of the instrument is 3D printed and can be fabricated using any standard desktop FDM 3D printer and assembled by hand. The instrument showed 86% accuracy in preliminary testing at a calibrated tipping resolution of 120ml. The cost of prototyping was 100$ - 120$, thus proving cost-effective for accurate hydrological budgeting as compared to the cost of the nearest available commercial solution (~1800$). Through our research and product design, we intend to reduce the barrier of entry and simplify the steep learning curve faced in developing hydrological instrumentation.

A document by Valerio Filice. Click on the document to view its contents.

aInstitute of Environmental Management, Faculty of Earth Science, University of Miskolc, 3515 Miskolc- Egyetemváros, Hungary; [email protected]; [email protected]; [email protected]. bGeology Department, Faculty of Science, Beni-Suef University, Beni-Suef, 65211, Egypt;  [email protected] * 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 – SouthAbstract 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 Jakob Christiaanse. Click on the document to view its contents.

Number: 1424249 Marginalized vulnerable coastal communities living along the urban coasts are continuously under the dual threat of natural hazards and the adverse impact of infrastructure development, which results in the increase of cumulative risk for these communities. Further, the irreversible impacts of climate change have also exacerbated the risks associated with aging infrastructure and vulnerable coastal communities. Therefore, strengthening the climate resilience of such communities stands as a duly acknowledged priority for developing nations. One of the possible solutions to strengthen climate resilience is through the development and implementation of sustainable hybrid infrastructure alternatives. In this work, we characterized the data-driven Coastal Infrastructure Resilience Index (CIRI) to assess the performance of existing coastal infrastructure along the coast of Mumbai City in India. This study thoroughly utilized the potential of high-resolution remote-sensing imagery and socio-economic parameters from SEDAC data to derive CIRI. The robustness of the CIRI is improved with integrated value function and expert knowledge. As both grey infrastructure, such as seawalls, levees, and bulkheads and green infrastructure, such as salt marshes, mangroves, beaches, dunes, oysters and coral reefs have limited resilience in a multi-hazard environment, we identified the major hotspots of concerns through CIRI to propose the plausible hybrid (green-grey) infrastructure alternatives (green-grey) using Adaptive Gradient Framework for Mumbai’s coastal context. Adapting Hybrid infrastructure alternatives empowers coastal communities with heightened climate benefits and co-benefits. The major findings of this study contribute as a science-policy instrument to localize the Sustainable Development Goals 11(11.5, 11. b), 13, and 14.2 of the United Nations.Keyword- Integrated Coastal Management, Adaptation, risk-informed, Urban coastal areas, decision-analysis

GRACE(-FO) monthly products are produced routinely using measurements of the orbital motion and the K-Band measurements of the distance between the two satellites. From these solutions, we gained great insight into the water cycle, ice mass balance, and ocean currents for the past twenty years. But these monthly solutions are not the full picture. More information about sub-monthly processes can be extracted from the Level-1B data. Currently, the data used to extract the GRACE-FO orbits contains the K-band ranging (KBR) measurements but not the data from the Laser Ranging Interferometer (LRI). It has been shown several times that using orbits and LRI data, one can calculate residual Line-of-sight gravity signals. These studies mainly focused on the Amazon River region to study changes in the water storage which happen faster than over one month.We use this approach to look at the changes in the speed of the llulissat glacier in Greenland. This is one of the biggest and fastest glaciers in Greenland and significant changes in its speed can happen over as little as one week during exceptional summer melt periods, as demonstrated a.o. by SAR interferometry. We demonstrate how LRI can help to give further constraints on when these changes in speed happen, within the restrictions of the limited spatial resolution.

The Gulf of Mexico (GoM) is a passive rift margin that is shrouded in thick sedimentary layers, making it challenging to trace its Mesozoic evolution. Traditionally, plate tectonic models have required an assumption of rigid plates, limiting our ability to understand the evolution of passive margins given the wealth of geological and geophysical evidence indicating significant crustal deformation during rifting processes. However, recent advances have been made in our ability to incorporate deformation into plate tectonic models.Here, we present a novel approach to reconstruct the evolution of the GoM by using an optimized and focused deformable plate model. Our new model uses a time-evolving focused deformation along the rift, where the strain rate evolves over time from being more uniform initially to increasing exponentially seaward to the point of continental rupture and ocean crust formation.By incorporating time-evolving deformation into our plate reconstruction, we can additionally derive crustal thickness and thermal and tectonic subsidence through time, which allows us to better explore the depositional history of the presalt deposition and crustal architecture evolution of the GoM basin. Our deformation model is optimized to minimize the root mean squared error (RMSE) between predicted present-day crustal thickness and the GEMMA crustal thickness model, resulting in an RMSE of 5.6 km compared to GEMMA, with <2 km absolute error in the northwest and northeast GoM. The resulting tectonic subsidence of ~1.5 km before the Yucatán block drifted in Late Triassic providing routes for the deposition of red beds through the paleo-drainage systems of the northern GoM as successor basin infilling. We find rapid subsidence occurred in the central GoM during the Early Jurassic shifting red bed deposition to a location that presently lies beneath the salt formation, potentially reconciling ~40 Myrs of missing sedimentary strata. Extension rate and stretching factor calculations reveal a transition from a magma-rich to a hyperextended margin, with possible mantle exhumation.Through our study, we highlight the significance of adopting optimized deformable plate reconstruction models, which enables quantitative interpretations of the tectonic history and geological evolution in rift basins globally.

The Cameroon Volcanic Line (CVL) and other tectonic features in Cameroon remain enigmatic, prompting ongoing debates about their detailed structure, composition, and geodynamic evolution. To shed light on these complexities, we leverage the ambient noise tomography (ANT) method to invert shear wave velocity (Vs) and image subsurface structures, providing crucial insights into both subsurface geology and deep crustal processes. Specifically, we employed two different methods: Markov chain Monte Carlo (MCMC) and Evolutionary Algorithm (EA) inversions to robustly constrain the Vs velocity structure, Vp/Vs ratio, and density beneath the CVL and its surrounding area.Our results reveal a prominent high-velocity structure at depths of 25 to 35 km, which precisely aligns with the CVL. Within this region, Vs velocities reach up to 4.0 km/s, accompanied by a Vp/Vs ratio ranging between 1.85 and 1.88 and density varying from 2.9 to 3.1 g/cm3. These characteristics suggest the presence of cooled mafic material that has intruded the crust. Our 2D depth cross-sections along the CVL indicate that these cooled mafic intrusions are ubiquitous along the entire volcanic line. However, they are spatially separated from the upper crust's volcano-plutonic structure by a thin intermediate structure exhibiting a Vp/Vs ratio of 1.68 to 1.71 and an average Vs velocity of 3.8 km/s, indicative of felsic to intermediate crust, which may be linked to the Pan-African Orogeny.The high Vp/Vs ratio and Vs velocity structures are found closer to the surface in the recently active volcanic provinces, accompanied by a thinner low Vp/Vs structure. We posit that this thinned low Vp/Vs structure may have facilitated the ascent of mafic material, contributing to recent volcanic activity in the region. Conversely, beneath the Oubanguides belt and Congo craton, these low Vp/Vs structures appear thicker, with mafic intrusions present at depth > 35 km. This observation suggests a dynamic process involving the pushing and exhumation of lower crustal material by the mafic material.Our crustal imaging results hold significant implications for our understanding of the region's geodynamic evolution, suggesting an interaction with deeper structures, may be responsible for the crustal intrusions and volcanism observed along the CVL.

Observational investigations of Earth's bow shock have highlighted distinct variations in turbulence characteristics when comparing fluctuations in the shock transition with those in the upstream and downstream plasma regions. To gain a more focused understanding in each of these areas, we have examined a range of local 2D and 3D hybrid simulations, using kinetic ions and fluid electrons. Each simulation has been chosen to cover a range of shock geometries, from quasi-parallel to quasi-perpendicular and high to low Mach number. In-situ observations, such as those from the Magnetospheric Multiscale (MMS) mission, are often unable to fully disentangle spatial and temporal effects. This is particularly evident in the shock transition and the magnetosheath, where, for example, whistler waves may have speeds comparable to the bulk flow and thus locally violate Taylor’s hypothesis for kinetic-scale fluctuations. Simulations overcome these limitations, enabling us to model the evolution of turbulence in the shock transition and further downstream. We characterize the turbulent fluctuations using the following three methods: Firstly, we examine the magnetic spectral indices spanning the inertial range and extending into the ion range as they change across the shock. Secondly, we investigate intermittency by means of the scale-dependent kurtosis. Lastly, we quantify the correlation lengths as measured across the shock, offering insights into the physical dimensions of fluctuations at scales smaller than the shock width. We will discuss the application of these measures to simulations in understanding the kinetic-scale behaviour of turbulence at Earth's bow shock.

Speculations extend the opportunity space of possible future climates by increasing the potential to provide plausible estimated qualities and quantities to further scientific research and aid engineering solutions. This novel work outlines the first steps to achieving an Anthropocene reversal that completes in Zoomers’ lifetimes — by 2100. The novel experimental high-scale carbon removal pathway, which was studied in MAGICC 6.8, required CDR to counterbalance all accumulated anthropogenic emissions since 1750 to return to preindustrial temperature (0.07ºC over the 1720-1800 and 0.14ºC over the 1850-1900) means by 2100 and complete GHG phaseouts by 2077, excluding Ammonia. The experimental pathway set extreme front loading of emissions reductions to reach net zero, and avoid tipping points, then achieve scaled removal to reach 300 ppm of CO2 concentration by roughly mid-century. This work’s findings recommend exploring carbon removal of cumulative anthropogenic emissions totaling 600 GtC to 775 GtC on a recent model ensemble with 1.55 to 1.7 times preindustrial CO2 concentration driven by forcings from emissions and calibrate to reproduce present-day temperatures to provide more detailed projections of temperature, holding below 1.5ºC, regional temperatures, below ground CO2 mineralization, sea-level rise, ENSO, AMOC, and jet-stream turnover, evolve. Continued fossil-fuel use is unable to yield complete emissions phaseouts or deep removals necessary to match a preindustrial climate. The findings support the utmost urgency to attain a maximally scaled sustainable zero-carbon intensity green growth development. And reinforce the increased global commitment to achieve net zero sooner and to avoid setting off more climate tipping points. The possibility of reaching a preindustrial climate should help inform the debate of maximally scaled sustainable green growth development for the fastest path to net zero, phase out of anthropogenic emissions sources, and scaled carbon removals with zero-carbon intensity to develop a more equal future world.

Sea ice ridges are an important morphological feature that stabilize shorefast ice across the Northern Alaskan coastline. This stability is important to local communities and ecosystems that rely on this habitat for food security and safety. Investigating the development of shorefast ice around Utqiagvik, AK, we describe an approach to identify grounded ridges throughout the winter season. To do this, we utilize high resolution altimetry data from NASA’s ICESat-2 satellite which provides unprecedented along-track detail that allows, for the first time, the detection of individual pressure ridges. We apply the University of Maryland Ridge Detection Algorithm (Duncan and Farrell, 2022) using ICESat-2 elevation data to identify and calculate ridge sail heights along each satellite track. From these heights, we estimate the depth of the ridge using sail/keel height ratios described in the literature. The calculated ridge depths are compared with high-resolution bathymetric data (NCEI Digital Elevation Model Mosaic) to classify potentially grounded ridges. This methodology for identifying and quantifying grounded ridges in shorefast ice will improve our understanding of coastal ice processes in a changing environment. 

The present study tries to assess and mitigate the geohazard-prone area in Barora Coalfield using geophysical methods. Barora being, a significant subsidiary of the Jharia coalfield, plays a crucial role in India's growing energy demand. The study area is affected by coal fire in the subsurface as illustrated by its thermal images. To evaluate subsurface conditions, magnetic data was taken initially. The magnetic data was first then reduced-to-pole and further centroid method was applied to determine the curie depth. The average curie depth was found to be approximately 10.74±0.9 meters, indicating active coal fires beneath this depth. Furthermore, using the multichannel analysis of surface wave (MASW) method, a significant change in velocity gradient was observed at a depth of around 11 meters, indicating variations in the velocity layers. To gain a comprehensive understanding of the subsurface, the ambient noise tomography method was employed. This revealed the presence of a coal seam at an approximate depth of 30 meters and also indicated the existence of abandoned underground galleries below 40 meters depth. Appending the dispersion curve from both seismic methods, we determined that the active coal seam at approximately 30 meters depth extended around 15 meters and was significantly affected by coal fires. The outcomes established the presence of underground galleries, oriented in the NE-SW direction, leading toward the SouthEastern railways which might be prone to subsidence.

Identification of global methane (CH4) sources is critical for the quantification and mitigation of this potent greenhouse gas. In preparation for Carbon Mapper, a future spaceborne imaging spectrometer mission, our work so far has focused on developing a robust methane plume detection method with AVIRIS-NG Columnwise Matched Filter (CMF) data. While we have previously demonstrated robust classification of plume source presence in ~800m square tiles, a deployed Science Data System (SDS) pipeline will require heat map or mask products that highlight the location of methane plume sources in entire flightlines and scenes.We present two methods for implementing pixel-wise methane plume detection. First, we convert our existing GoogLeNet Convolutional Neural Network (CNN) classifier into a Fully Convolutional Network (FCN) segmentation model with a novel implementation of shift-and-stitch, in which the final fully connected layer is replaced by a one-by-one convolutional layer. This allows us to produce a saliency map of methane plumes in scenes of arbitrary sizes without re-training the existing classification model. While heatmaps produced by this method lack pixel-wise precision, they are sufficiently localized to direct attention for further review.Second, we propose a new hybrid segmentation model based on the popular U-Net architecture. Notably, we backpropagate both segmentation and classification losses during training, which significantly reduces the number of false positive plume detections due to false enhancements and artifacts. Additionally, we successfully use algorithm-generated weakly-labeled segmentation masks, mitigating the need for expensive human-generated segmentation annotations. We report and compare the scene-level detection performance of these two methods on previously curated datasets from “COVID” (2020 California), “CACH4” (2018 California), and “Permian” (2019 Texas) AVIRIS-NG Campaigns.