As all kinds of physics-based and data-driven models are emerging in the fields of hydrologic and hydraulic engineering, Bayesian model averaging (BMA) is one of the popular multi-model methods used to account for the various uncertainty sources in the flood modeling process and generate robust ensemble predictions based on multiple competitive candidate models. The reliability of BMA parameters (weights and variances) determines the accuracy of BMA predictions. However, the uncertainty in the BMA parameters with fixed values, which are usually obtained from the Expectation-Maximization (EM) algorithm, has not been adequately investigated in BMA-related applications over the past few decades. Given the limitations of the commonly used EM algorithm, the Metropolis-Hastings (M-H) algorithm, which is one of the most widely used algorithms in the Markov Chain Monte Carlo (MCMC) method, is proposed to estimate the BMA parameters and quantify their associated uncertainty. Both numerical experiments and the one-dimensional HEC-RAS models are employed to examine the applicability of the M-H algorithm with multiple independent Markov chains. The performances of the EM and M-H algorithms in the BMA analysis are compared based on the daily water stage predictions from 10 model configurations. The results show that the BMA weights estimated from both algorithms are comparable, while the BMA variances obtained from the M-H MCMC algorithm are closer to the given variances in the numerical experiment. Moreover, the normal proposal distribution used in the M-H algorithm can yield narrower distributions for the BMA weights than those from the uniform prior. Overall, the MCMC approach with multiple chains can provide more information associated with the uncertainty of BMA parameters and its prediction performance is better than the default EM algorithm in terms of multiple evaluation metrics as well as algorithm flexibility.

Mapping global vertical vegetation structure (VS) is critical for the quantification of global carbon stocks. While orbital LIDAR measurements of NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission provide direct estimates of vertical VS, they only cover 4% of the global land surface. Recent works have produced global contiguous maps of canopy height using convolutional neural network (CNN) models and satellite imagery. However, these models faced some challenges estimating high canopy heights (>30 m) and identified some tiling artifacts (Lang et al., 2022; Potapov et al., 2020).We present various methods to address these limitations. First, we remove tiling artifacts by using overlapping tiles when producing maps and removing zero-padding from CNN architectures. Second, we compare the benefits and limitations of a few different methods to improve high canopy height estimation. Among the methods is histogram matching predicted heights to nearby LIDAR measurements. Another is learning a calibration model to correct each pixel based on similar known measurements nearby. Finally, we borrow recent advances in deep depth completion from the autonomous driving field to create an integrated model that uses known values to improve the prediction map. To demonstrate these methods, we map global vertical VS with a CNN model at 1km resolution using observations from Landsat, L-band synthetic aperture radar observations from the Advanced Land Observing Satellite (ALOS) PALSAR-2, and surface topography. For model training, GEDI relative height metrics are filtered and aggregated into 1km grids. We also use measurements from the Ice, Cloud and land Elevation Satellite 2 (ICESat-2), inter-calibrated with GEDI using co-located measurements. Finally, we apply the aforementioned corrective methods to the product, reporting global RMSE and MAE metrics, as well as visual qualitative observations. These results open the path for unsaturated global vertical VS products at higher 500 m, 200 m, and 100 m resolutions.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.

Key Points:Lagrangian-based HYSPLIT modelling system used to estimate volcanic ash particle trajectories.HYSPLIT simulation took place before and after the massive eruption on 15th January 2022 (termed as pre-caldera and post-caldera respectively in Section 5)Volcanic ash particle deposition and volcanic ash particle position simulated using HYSPLIT for the HTHH submarine volcano massive eruption event.AbstractVolcano-seismic signals such as long-period (LP) events and tremors are important indicators for volcanic activity and unrest. Explosive volcanic eruptions are stunning phenomena that influence the Earth’s natural systems and climate in a variety of ways. This paper discusses the mid-week January 2022 eruption of the HTHH submarine volcano, especially on 15th January an event with many impacts in the region (dynamic, chemical, climate breakdown). Given the potential for a volcanic eruption to affect climate, the oceanic system, or climate variability, consistent and understandable modelling of these exceptional events is critical.The main objective was to determine the volcanic effects in our atmospheric boundary layer (ABL) during the multiple eruptive events occurred on January 2022 at HTHH. Our discussion also contributes to understanding the underlying Earth system dynamics triggered by cataclysmic volcanic eruptions. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model system developed by the National Oceanic and Atmospheric Administration’s (NOAA) Air Resources Laboratory was used to deliberate the effects caused by the multiple eruptions of HTHH on mid-week of January 2022. Our modelling results include model trajectories at different frequency levels, volcanic ash deposition and ash particle position from the series of multiple eruption events of submarine volcano HTHH in the mid-week of January 2022.

Thawing of permanently frozen ground (permafrost) has increased in recent decades with negative implications for human and non-human adaptation to climate change. Impacts include reduced ground stability, increased transportation risk, and changes in water availability. Direct measurements of permafrost active layer thickness (the depth of thawed ground overlying permafrost) are sparse. Measurements currently exist for a few hundred sites located primarily in the Northern Hemisphere supported by the Circumpolar Active Layer Monitoring (CALM) Program. The sparsity of direct active layer thickness measurements limits broad-scale understanding of changes in permafrost thaw and confidence in future projections. To address the sparsity of direct active layer thickness measurements, we developed a method to estimate active layer thickness change from streamflow measurements, which integrate processes over broad spatial areas and are more common than point-scale active layer thickness measurements. The method uses classical principles of hydraulic groundwater theory and nonlinear baseflow recession analysis, which sets it apart from prior methods based on linear recession analysis. The method is applied to catchments in the continuous and discontinuous permafrost zone of the North American Arctic containing co-located streamflow and CALM active layer thickness measurements. We find good agreement in the magnitude and direction of measured and predicted active layer thickness trends. This suggests that regional-scale estimates of active layer thickness change can be obtained from streamflow measurements, which may open the door to retrospective estimation of active layer thickness change in data sparse Arctic regions with short, sporadic, or even nonexistent ground-based active layer measurements.

Evaluation of the performance of hydrologic and hydraulic models is a crucial step in the modeling process. Considering the limitations of single statistical metrics, such as the Nash Sutcliffe efficiency (NSE), the Kling Gupta efficiency (KGE), and the coefficient of determination (R2), which are widely used in the evaluation of model performance, an evaluation framework that incorporates multiple criteria and based on the generalized likelihood uncertainty estimation (GLUE) is proposed to demonstrate the uncertainty in the evaluation criteria and hence to quantify the overall uncertainty of flood models in a comprehensive way. This framework is applied to the one-dimensional HEC-RAS models of six reaches located in States of Indiana and Texas of the United States to quantify the uncertainty associated with the channel roughness and upstream flow input. Specifically, the effects of different prior distributions of the uncertainty sources, multiple high-flow scenarios, and various types of measurement errors (white noise, positive bias, and negative bias) in observations on the evaluation metrics are investigated by using the bootstrapping method and Monte Carlo simulations. The results show that the model performances based on the uniform and normal priors are comparable. The distributions of all the evaluation metrics in the framework are significantly different for the flood model under different high-flow scenarios, and it further indicates that the metrics are essentially random statistical variables. Additionally, the white-noise error in observations has the least impact on the metrics, while the positive and the negative biases would have opposite impacts, which depends on whether the model overestimated or underestimated the hydrologic variable.

A document by Zahra Zali. Click on the document to view its contents.

A document by Fanny Lehmann. Click on the document to view its contents.

Poem to imagine the “essence” of water as it circulates through the Earth universe, synergistically supporting all environments and living ecosystems, forming, and shaping land and life. The poem links key elements of the interactive global water cycle and international programs to sustainably manage natural, and socioeconomic resources, given the challenge of climate change. It is in awareness of: –Essential Water Variables (EWVs) of the Group on Earth Observations (GEO) Global Water Sustainability (GEOGLOWS) initiative; Earth Observations (EO) for the Water-Energy-Food Nexus (EO4WEF) community activity; UN Sustainable Development Goals (UN SDGs), UNFCCC–Climate Change. The poem hopes to bring water to the forefront of consciousness. Readers are invited to comment on the intangible “feelings” evoked by the poem.

Animal behaviour such as dispersal and migration ensure their survival in the landscape. It has been established in the past few decades that wildlife conservation and study of their movement in the wilderness is vital for sustainable ecosystem. Thus, identification of regions having high movement permeability for planning and maintenance of functional wildlife corridors has turn out to be a fundamental requirement for habitat management. This study emphases on movement of big cats-Bengal Tiger (Panthera tigris tigris) and Leopard (Panthera pardus fusca) in the protected area of Rajaji National Park situated in Uttarakhand State of India. The National park is a designated tiger reserve with large amount of tigers and leopards at its disposal. Here, Circuitscape was used to generate connectivity map of the study area. The results were validated using occurrence points downloaded from GBIF. The habitat suitability and resistance of the landscape was estimated based on literature review and expert opinion survey. Since, both the species have comparable ecological niche, similar habitat parameters were used for generation of resistance map of the species. Occurrence points for the species were downloaded from GBIF. 60% of the points were used as nodes or focal points where species presence is recorded whereas 40% of the points were used in validation of the connectivity paths. Results depicts the current density map of the study area highlighting areas with high connectivity for the species.

Temperature is a key physical variable in streams that controls rates of metabolic processes and oxygen availability, and therefore the suitability of aquatic ecosystems. During the summer low flow period, stream temperature can be moderated by contributions from cool water sources, such as groundwater discharge and higher elevation headwaters. However, the relative contribution of these cool water sources can be spatially and temporally varying, particularly in snowmelt-dominated, high-relief watersheds. In this study, in situ and remote sensing methods are used to measure the stream temperature along a low elevation section of the North Alouette River (British Columbia, Canada) that passes through a forested area and into an open agricultural area. The methods include temperature loggers placed at the stream surface and streambed interface, and thermal infrared images acquired using a drone and Landsat 8 and 9 satellites. The drone and in situ measurements of stream temperature show good agreement, while the satellite images show the same temperature distribution (cooler in the forested area and warmer downstream in the open agricultural area) but overall shifted temperatures. Areas of mixing of cool and warm waters are identified within the stream channel using the drone imagery. Waters samples analyzed for stable isotopes are used to identify the different source waters and estimate their relative contribution to stream temperature moderation. This fingerprinting is made possible by a precipitation isotope composition-elevation gradient in the catchment. The isotope data support the observations of mixing identified with the temperature data. Understanding of where and when cool water sources contribute to streamflow will be used to inform groundwater allocation decision-making, to ensure that groundwater pumping is minimized in areas where groundwater discharge is critical for moderating stream temperatures.

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

A document by Niloufar Nowrouzi. Click on the document to view its contents.

V. S. Gokul 1,2,3., K. M. Sreejith4., G. Srinivasa Rao 5., M. Radhakrishna 1*., P. G. Betts 21 Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai 400 076, India.2 School of Earth, Atmosphere and Environment, Monash University, Clayton, Victoria 3800, Australia.3 IITB-Monash Research Academy, Indian Institute of Technology Bombay, Mumbai 400 076, India.4 Geosciences Division, Space Applications Centre, Ahmedabad 380 015, India.5 Department of Applied Geophysics, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad 826 004, India (*Corresponding author:[email protected])AbstractThe Indian Ocean has the largest geoid anomaly, known as the Indian Ocean Geoid Low (IOGL). This long wavelength geoid depression has a magnitude of negative 106 m, and is centered south of India. The nature and depth of the sources causing this characteristic low are poorly constrained and has been the subject of debate. In this abstract, we focus on understanding the density contributions to the geoid low from the crust and upper mantle using joint analysis of geoid and gravity data along with published tomographic models in the region. Decomposition of geoid anomalies in the spectral domain indicate that mass anomalies below the upper mantle (> 700 km) contribute to 90% of the total geoid anomaly. In order to compute the upper mantle contribution to the IOGL, we used the Moho geometry and the crustal density structure from the 3-D gravity inversion, and the SL2013sv tomography model for the upper mantle density structure. The presence of density sources, which was not resolved in the modeling within the sub-lithospheric mantle is confirmed upon comparing the crustal and upper mantle (up to 700 km) geoid response below the IOGL with n=10 residual geoid anomaly. Integrated gravity-geoid 2-D modeling of the geometries of the anomalous sources located at the base of LAB and at a depth of 320-340 km, respectively, confirms that the contribution of density structures up to 700 km explains only the ten percent of the IOGL which matches well with the spectral decomposition results. This suggests that the lower mantle sources, such as paleo-subducted slabs or plume sources from the core-mantle boundary significantly contributes to the IOGL.

Identification of global methane (CH4) sources is critical to the quantification and mitigation of this greenhouse gas. Future imaging spectrometer missions, such as Carbon Mapper, will provide global, spatially resolved observations that will make it possible to accurately map methane sources. However, the sheer data volume of these missions make manual source identification infeasible, and expected artifacts in matched filter methane plume identification challenge simple thresholding. Recent works have demonstrated the feasibility of Convolutional Neural Networks (CNNs) for plume detection; however, in the past, these models have suffered from high false positive rates and were limited in their training and evaluation to individual flight campaigns.We have assembled quality-controlled tiled datasets from three Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) campaigns: a 2020 California campaign (“COVID”), a 2019 Texas Permian Basin campaign (“Permian”), and another 2018 California campaign (“CACH4”). These datasets are notable for their diversity of surface conditions, spatial resolutions, and source types (oil & gas, energy, waste, livestock). Labeled methane sources in these datasets have been manually verified, and flightlines with systematic artifacts have been filtered out. We trained a GoogLeNet CNN classifier model on each of these campaigns to evaluate intra- and inter- campaign performance. We also trained a model on all three campaigns and evaluated its performance on each dataset. We observed an F1 performance of 0.7 or greater for each model trained and evaluated on its own dataset. We also observed that the model trained on all three datasets often outperforms individual models on multiple metrics. Finally, we converted the model into a fully convolutional network (FCN) for methane plume saliency map generation. We plan to extend this work to datasets acquired by the Global Airborne Observatory (GAO) and prepare a model for deployment for the Carbon Mapper orbital data product pipeline.

The ILRS contribution to ITRF2020 is a time series of weekly/bi-weekly SINEXs with station position estimates and EOP, from 7-day arcs (1993.0 – 2021.0) and 15-day arcs over 1983.0- 1993.0. Each solution was obtained as the combination of loosely constrained individual solutions from the seven ILRS Analysis Centers: ASI, BKG, DGFI, ESA, GFZ, JCET and NSGF. Everyone followed strict standards agreed within the ILRS Analysis Standing Committee (ASC) and used SLR data from LAGEOS, LAGEOS-2, Etalon-1 and Etalon-2, (LAGEOS-only from 1983 to 1992). The ILRS ASC devised an innovative approach in handling systematic errors in the network, never before utilized. After a 5-year pilot-project documented in Luceri et al., (2019). The Station Systematic Error Monitoring PP (SSEM), delivered a series of long-term mean bias estimates for each station, the time intervals of applicability and their statistics. They were derived from freely adjusted station position and EOP solutions for the period 1993.0 to 2020.5, using the latest satellite CoG model. The simultaneous estimation of the station heights and measurement biases resulted in a self-consistent set of weekly bias estimates for each site. Breaks and “jumps” were used to decne the periods of applicability and to calculate the mean bias and its standard deviation. The mean biases were pre-applied in the re- analysis, limiting the remaining jitter of the bias to negligible level. This approach strengthened the estimation process without a compromise of the cnal results’ accuracy. As a result, the ILRS contribution to ITRF2020 minimized the scale difference between SLR and VLBI to below 2 mm (ITRF2014 ~9 mm). We present an overview of the procedures, models, the improvement over previous ILRS products, focusing especially on the Core ILRS sites, and an overview of how the new model has been implemented in support of the ILRS ogcial products.

A document by Yuchen Lu. Click on the document to view its contents.

Most studies on the impacts of extreme hydrometeorological events on hydrological processes have focused primarily on surface water systems rather than groundwater systems. This study explores and seeks to untangle the complex nature of groundwater dynamics and resilience across British Columbia (BC) in response to the 2021 heatwave-intensified drought and atmospheric rivers (ARs). Historically, there have been many episodic drought events along with substantially wet periods. However, 2021 marked an unprecedented year for the immediate co-occurrence of intense and extreme drought and deluge. This weather whiplash resulted in the lowest and highest groundwater levels on record for many wells across BC. The record meteorological drought, intensified by a 13-day heatwave in late June, affected the entire province and lasted for over 50 days in the south coast region. This was followed in November by the most intense ARs to make landfall on record in southwestern BC. Groundwater hydrograph anomalies for 2021 were computed relative to their short-term historical mean for 194 provincial observation wells across the province. The 2021 anomalies showed a limited but distinct range of responses to both the drought and ARs, and cluster into three response groups, largely associated with their respective hydroclimatic regime. Many coastal wells showed a strong response to drought; however, nearly all wells in the southern interior responded substantially, with groundwater levels significantly below their historical range by late summer. Presently, groundwater levels seem to have recovered across the province, especially on the coast. This resiliency is attributed in part to the ARs that made landfall since last year along with a particularly wet, La Niña winter. The majority of coastal wells showed a much stronger signal to the ARs compared to the interior or eastern BC wells, likely due to the more rapid and intense rainfall experienced in southwestern BC. Groundwater systems across BC were variably impacted by these hydrometeorological extremes, showcasing the need for focused and area-specific approaches to water allocation decisions in assuring sustainable withdrawal practices.