With a warming climate, solar activity including Sunspot Number (SSN) and large-scale climate phenomena including EI Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Arctic Oscillation (AO) have induced changes in climate extremes and changes in the hydrological cycle in arid-semiarid regions of the world, thus a detailed investigation of climate variability can play a key role in water resources management, drought monitoring, ecological restoration and sustainable development. In this study, we used wavelet coherence (WTC) based on continuous wavelet transform (CWT) to assess the impacts of SSN, ENSO, PDO and AO on multiple interacting hydrological processes and identify the teleconnection patterns and lead-lag relationships between the four principal modes and changes in extreme temperature and precipitation events, meteorological drought, and streamflow variability in Xinjiang, an arid-semiarid region of China. The results indicated that solar activity and climatic oscillations were viewed as the primary drivers for periodic variation of extreme temperature events and the evolution of drought in Xinjiang. For instance, the ENSO positively affected warm extremes with intermittent coherence in the 2–6-year band during 1984–2000 and had negative correlations with cold extremes in the 2–6-year band at the interannual scale. Compared with warm extremes, variability in cold extremes was much more sensitive to the activity pattern of AO. It was clear that the coherence of temperature variables in Xinjiang with PDO was weaker than that with ENSO and AO, and there was a nonsignificant covariance between PDO and extreme temperature events. In addition, the WTC spectra showed that teleconnection factors including solar activity and three large-scale climate phenomena had significant impacts on annual and monthly drought evolution, and AO had the strongest influence on annual standardized precipitation evapotranspiration index (SPEI) values. In general, compared with SSN, ENSO, PDO and AO all showed clear leading effects on precipitation extremes variability and annual streamflow variability for a specific time and frequency, and solar activity’s influences might be transferred by ENSO to precipitation extremes or streamflow variability at the 2–7-year band. Overall, the warming and wetting trend in Xinjiang may be a local manifestation of global multivariate climate change. Thus, our findings will have important implications for designing best practice strategies for water resource management and ecological restoration in similar arid-semiarid basins around the world.

Man-made terraces for agricultural purposes are a quite diffuse and ancient anthropogenic landscape modification in mountainous areas. The original slope alteration, obtained through a sequence of sub-vertical and sub-planar surfaces, represents a human interference with the geomorphic system, altering the original balance of geomorphological and geo-hydrological factors. Stone walls and soil formed by human activity have been artificially immobilized on the slopes and are available again to gravitative processes once in abandonment and may be subject to deep degradation in case of intense rain events. However, socio-economic conditions play often a crucial role in the abandonment of terraces, indirectly contributing to increase gully erosion and walls failure. The modification of the original slope profile, due to its regular geometry in respect to the typical more complex natural surface, is rather suitable to be detected through remote sensing, particularly LIDAR, as many authors have recently demonstrated. In the present research the attention has been focused on the assessment of terraces and of the volume of stones and soil that have been involved by human activity. The research area is among the most deeply modified by terraces in the Mediterranean area and internationally famous for this landscape anthropogenic alteration: The Cinque Terre in Italy is a National Park intensively visited by tourists all over the year. Then terraces represent an important economic asset that need to be preserved from degradation and collapse as partially occurred in 2011 after the intense rain event that caused flood, hundreds of landslides and consequently damage. During the 2011 event many terraced slopes have collapsed with significant loss of soil and stone walls: the research allowed to evaluate the lost volumes and to estimate the remaining ones in the Vernazza catchment.

Some aspects of the dynamics of aeolian transport over a flat sediment bed have been thoroughly investigated and are relatively well understood. The interactions between grains in transport and the wind give rise to well-known dynamical scaling laws for the fluxes and concentrations of grains in most of the transport layer. However, recent work has revealed a sudden shift in these scaling laws near the granular surface and below. While the vertical flux of grains in the transport layer scale linearly with excess wind shear stress, the vertical flux near the granular surface—the ‘erosion rate’—scales linearly with wind speed. Analysis of numerical modeling results reveal that near-surface horizontal and vertical fluxes are important for the instability that leads to wind ripple growth and stabilization as well as ripple propagation. A few main open questions are: What are the physical mechanisms behind the scaling of the erosion rate with wind speed? Could they arise from the small subpopulation of high-energy grains, who’s characteristics scale differently than the average grain in transport? As these grains move downward from the free-wind layer, do they tend to retain their properties as they pass through the feedback layer, delivering their energy, momentum and scaling directly to the bed? Do collisions between grains near and within the bed, which redistribute energy and momentum from high-energy impacts, play a key role in determining the scaling of near-bed fluxes? How important are potential collective effects that can occur when impacts with sufficient energy to excite the bed occur close together in time and space? An answer to these questions would help complete our understanding of the physics of aeolian transport, with repercussions that shed light onto the emergence and propagation of wind ripples. Using a detailed grain scale numerical model, we are investigating the dynamics of grains near the granular bed, and what saltation properties drive these dynamics. Preliminary results, including velocity distributions near the bed, indicate that the signal from high-energy grains that traverse the feedback layer from above reaches the bed surface, consistent with the hypothesis that the surface erosion rate is related to this small population of grains who’s characteristics scale with the free-wind speed.

Geothermal well log analyses consist of utilizing pressure and temperature data measured along the wellbore to predict feed zones, reservoir temperature, and reservoir pressure. Interpreting geothermal production logs can be subjective and require great expertise to achieve repeatability. In situations where there may be several log data, interpreting the logs may be time consuming for quick decision-making processes. This work discusses the implementation of a multi-layered deep learning convolutional neural network to automatically diagnose sets of temperature and pressure well logs. The algorithm achieves results similar to those of a professional engineer. This algorithm enables the interpretation of many well logs in just a few seconds. Data input for this project is synthetic well data that mimics real data. 10,000 datasets were used. The data was split as follows: 9,800 and 200 for training and validation respectively. The algorithm used takes as input three “depth-series” logs of temperature, pressure, and temperature gradient, passes the data through a convolutional neural network including a flat layer and then a fully connected layer with five output variables which are the depths of the feed zones, the reservoir temperature, the reservoir pressure and the depth at which the reservoir pressure is known. The cost function for this model was the mean squared error. The optimizer algorithm used was Adam, and the learning rate had an exponential decay. The algorithm recorded the model state that had the lowest mean absolute validation error. The architecture was implemented in Keras with a TensorFlow backend. The best model found during the process of hyper-parameter tuning was used to predict the reservoir characteristics for the validation and testing data sets. The results show a good match between the predicted data and actual data with a training error of 0.9%, validation error of 2%, and test error of 7%. Future works will involve adding more real data to the training and validation set, increasing the number of feedzones that can be identified, and performing sequential analysis using interdisciplinary data.

The study aims to investigate the relationship between dominant climate variabilities namely El Niño-Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD) versus rainfall, and vertical humidity in Chiangmai, Thailand. The Oceanic Niño Index (ONI) which represents ENSO status and Dipole Mode Index (DMI) which represent IOD indicator were collected for the period 1986-2020 (35 years). Subsequently, the 12-month lag correlations were analyzed between both indicators against rainfall and vertical moisture at cloud-base and cloud-top height levels for the same time periods. The results indicate that the impacts of both climate variabilities may not occur during the same period of the phenomenon. The effects of ENSO to weather pattern in Chiang Mai might be lagged by 0 to 12 months. Even though the correlation of the humidity versus ONI and DMI is fluctuated. The correlation results of ONI and cloud-base humidity level has moderate values and consistent with La Niña events. However, the DMI correlations against rainfall and mixing ratio are inconclusive due to insufficient data. The relationship between ONI index, rainfall, and mixing ratio can be used to predict the future rainfall and atmospheric moisture once they are validated. Keyword: ENSO, ONI, DMI, IOD, rainfall, mixing ratio, vertical humidity

Muddy sediments are abundant across aquatic ecosystems, consisting of mineral grains and biological material. Erosional characteristics of these cohesive sediments are impacted by micro-organisms providing bio-stabilisation. Deposition may be impacted by chemical and biological composition, along with turbulence properties, which in turn influence flocculation of suspended particulate matter. Flocculation processes affect settling velocity, porosity and density characteristics. Cohesive sediments absorb contaminants, as well as influencing interactive processes between sedimentary dynamics and hydrodynamics, through their bio-physical attributes. It is therefore beneficial to predict muddy sediment transport processes via numerical modelling. Accurate modelling relies on quantitative erosional and depositional data for calibration. Through collation and analysis of field- and laboratory-derived data sets, this study examined aspects of erodibility and deposition across several aquatic environments (including estuarine, intertidal and lake sediments). A range of case studies examined floc properties, sediment composition, erosion thresholds, turbulent shear stress and suspended particulate matter concentration. Investigation of floc dynamics in estuarine sediments revealed larger, faster settling flocs in muddy sediment (mean settling velocity, Wsmean = 4.1-5.2 mm.s⁻¹; mean floc effective density, ρe,mean = 317-352 kg.m⁻³). In mixed sediment, flocs were smaller and settled more slowly (Wsmean = 3.8-4.0 mm.s⁻¹; ρe,mean = 288-508 kg.m⁻³). Comparison of oil-contaminated sediments revealed the importance of floc size class and mineral type. On the addition of oil, larger, faster settling flocs were produced in pure bentonite cases, while smaller, slower settling flocs were observed in kaolinite cases. In highly organic lake sediments (organic content = 62%), settling velocity varied over increasing suspended sediment concentration and between floc size classes (macroflocs faster than microflocs by 0.95 mm.s⁻¹). Such findings may be utilised to increase the understanding of complex sedimentary and hydrodynamic interactions within aquatic environments. This study provides quantitative data, applicable to the improvement of predictive numerical model reliability.

Climate is a key factor affecting water and energy resources. To quantify its effect, researchers have developed statistical and mechanistic models that simulate the interactions of the Water-Energy-Climate Nexus (WECN). In this work, we address two limitations of current WECN models, including the need to (1) account for the impact of climate on both demand and supply of water and energy, and (2) consider the influence of multiple climate variables in addition to temperature and precipitation. For this aim, we build upon our existing model that simulates in coupled fashion the water and energy systems of the metropolitan region of Phoenix, AZ. We design multidecadal storylines of future climate using an ensemble of downscaled general circulation models from CMIP6, along with possible energy infrastructure expansion scenarios. We develop a hydrologic model to account for the climate impacts on surface water supply sources. We constrain electricity supply based on water availability and temperature-dependent water intensities for power production. In parallel, we consider the effect of climate on demand through multilinear regressions between per-capita water and energy demand and several climate variables. This work advances the simulation of WECN interactions by integrating statistical and mechanistic models of water and energy systems with climate and hydrologic models. While the modeling framework is tested in the Phoenix metropolitan region, our findings provide useful insights that support WECN modeling efforts in other regions.

There has been a significant increase in the amount and accuracy of mineral data (from resources like Mindat, MED or the GEMI) and the improvements in technological resources make it possible to explore and answer large, outstanding scientific questions, such as, understanding the mineral assemblages on Earth and how they compare to assemblages and localities on other planets. In the last couple of years, affinity analysis methods have been used to:1) Predict unreported minerals at an existing locality, 2) Predict localities for a set of known minerals[1]. We’ve chosen to call this application “Mineral Association Analysis”[2]. Affinity analysis is an unsupervised machine learning method that uses mined association rules to find interesting patterns in the data. Most of the metrics used to evaluate market basket analysis methods focus on either the ability of the model to ingest large amounts of data[3], or using a metric based comparison of various algorithms used for association rule mining[4], or on evaluating the rules mined to more efficiently generate association rules[5]. However, when patterns generated in an unsupervised method are used to predict the occurrences of entities such as minerals, there needs to be a way to evaluate the predictions made by the model. It’s in such an area that there has been very little work. In this abstract, we explore the development of a new method to evaluate the results of association rule mining algorithms specifically when used when the association rules generated are utilized in a predictive setting. [1] Prabhu et. al (2019). In AGU Fall Meeting Abstracts (EP23D-2286). [2] Morrison et al. Nat. Geo. (2021) In Prep. [3] Agrawal et al. (1993) SIGMOD’93. [4] Sharma et al. (2012) IJERT 1(06). [5] Üstündağ and Bal (2014) Proc. in Comp.

Statistical downscaling plays an important role in reducing the uncertainty in regional-scale climate change studies. A major assumption of all statistical downscaling studies is the stationarity in the relationship identified from the historical observations. This assumption is difficult to validate since the future is truly unknown. The proposed methodology tries to overcome the limitations of this assumption by considering global physics that drive the regional climate to select the predictors to develop the statistical relationship. Since the statistical model is developed with climatic processes as a background, it can be said with higher confidence that the modelled relation would remain sound for the future as well. The proposed methodology is divided into two stages. In the first stage, a Relevance Vector Machine model is developed using historical observed global predictors that are identified to have teleconnections with the regional climate predictand. The spatial downscaling of this regional predictand has been done on a monthly scale. The bias associated with the downscaled predictand is removed by separating the anomalies from the prediction and adding that to the historical mean of the observed data. This monthly series is further disaggregated into daily series using a weather generator. The non-stationarity in the climate projections is accommodated in the weather generator and more regional features of the climate is integrated into the predictand in this stage. The proposed methodology is validated by downscaling rainfall over a river basin in India and its performance is analysed. The downscaled data is seen to reproduce characteristics of daily rainfall like consecutive wet days, the number of rainy days, wet spell duration, and dry spell duration with a bias of less than 10%.

The Neoproterozoic Era represents extreme environmental conditions with three major glaciation events. Associated with these glaciation and deglaciation periods are high amplitude positive and negative δ13C excursions which have been observed in the rock records, suggesting perturbations in oceanic biogeochemical cycles. However, whether these isotopic records reflect primary depositional signature and open ocean condition are debated. The Marwar Basin of the Indian Shield preserves one such record of the Ediacaran time period. The magnitude of negative δ13C excursion from the Marwar Basin is comparable with the Shuram excursion.1 We report elemental compositions of sixty-eight carbonate samples collected from five spatially distributed sections from the Bilara carbonates of the Marwar Basin. Selected samples from two of these sections were analyzed for their radiogenic Sr (87Sr/86Sr) and stable Ca (δ44/40Ca) isotopic compositions. Elemental compositions were measured using an Inductively Coupled Plasma Mass Spectrometer (ICP-MS, X series II) while 87Sr/86Sr and δ44/40Ca (reported relative to NIST SRM 915a) values were measured using a Thermal Ionization Mass Spectrometer (TIMS, Triton Plus), both at the CEaS, IISc Bangalore, India. The Bilara Group carbonates are sub-divided into two populations based on non-redox REY anomalies and the Y/Ho ratio. Super-Chondritic Y/Ho (40-52) and positive La anomaly (1.01-2.65) of some samples suggest deposition under open ocean conditions and connectivity to the global ocean. While heterogeneity in δ13C values is evident in samples with low Y/Ho (<40), samples with high Y/Ho (> 40) preserve low δ13C values. No significant correlation has been observed between δ13C and Ce anomaly (Ce/Ce*) suggesting absence of any paleo-redox gradient. The lowest 87Sr/86Sr (~0.7079) observed in the carbonates is comparable with Ediacaran seawater confirming retention of primary depositional signatures in these samples. These carbonates show heavy δ44/40CaSRM915a compositions (1.44‰-2.21‰) typical of Neoproterozoic post-glacial successions2. Our study confirms primary origin and open ocean nature of the late Neoproterozoic δ13C excursions in the Marwar Basin. [1] Ansari et al., (2018) Precambrian Research, [2] Silva-Tamayo et al., (2010) Precambrian Research

The subseasonal Madden-Julian oscillation (MJO) is among the most important modes of tropical variability on the planet and is a dominant driver of subseasonal-to-seasonal (S2S) prediction globally. The past decade has seen substantial advances in MJO prediction using dynamical forecast models, which now routinely outperform traditional statistical MJO forecasts (e.g. multiple linear regression models). At the same time, an increasing body of literature has demonstrated that machine-learning methods represent a new frontier in Earth science, opening the door to more advanced statistical forecast models of the MJO. In this study, we explore whether state-of-the-art machine learning methods can be used to make real-time MJO forecasts that outperform traditional statistical models and do comparably well to dynamical models. In particular, we utilize neural networks trained on observational tropical fields to attempt to make skillful forecasts of MJO convection out to several weeks lead time. Through contrasting the machine-learning models’ behavior with simpler statistical models and dynamical forecast models, we explore the advantages and disadvantages of statistical versus dynamical forecasts. A novel aspect of our analysis is the use of cutting-edge techniques to allow us to visualize how our neural network models makes their predictions. These techniques, such as layer-wise relevance propagation, can lead to new insights into regions of MJO predictability, allowing us to better interpret sources of MJO prediction skill within the machine-learning model. We further diagnose whether our machine-learning models contain well-known aspects of MJO prediction found in dynamical models, such as an increase in prediction skill during boreal winter or during certain phases of the stratospheric quasi-biennial oscillation.

The relationship between multiple hydroclimatic variables and vegetation conditions in the upper Madeira Basin (southwestern Amazon) has been analyzed. Vegetative dynamics are characterized using NDVI dataset as an indicator of the photosynthetic capacities of vegetation. Hydroclimatic variability is analyzed using satellite-based precipitation datasets, observed river discharge and satellite measurements of terrestrial water storage (TWS). Our results show that the vegetation in the Basin varies from energy- to water-limited. During the peak of the wet season (January-February), rainfall, discharge and TWS are negatively correlated with NDVI (r=-0.48 to -0.65), suggesting that during this period the vegetation is mainly energy-dependent. Outside this period, these correlations are positive (r=0.55 to 0.9), suggesting that vegetation depends mainly on water availability. This higher water dependence is more noticeable during the vegetation dry season (VDS; June-October). Considering the predominant land cover types, differences in the hydroclimate-NDVI relationship are observed. Evergreen forests remain energy-limited during the beginning of the VDS, but they become water-dependent almost at the end. Savannas show a different behaviour, where water dependence occurs months before the onset of the VDS. On the other hand, unlike the other variables, the TWS better explains the NDVI in evergreen forests during the VDS (r=0.7 to 0.85). This is probably because evergreen forests are more dependent on deep soil water. A spatial analysis between hydroclimatic variables and the NDVI shows the predominance of positive correlations in most of the basin. However, specific areas do not show significant correlations. The weak relationship in these areas is explained by two factors i) very wet conditions during most of the year in the “rainfall hotspot” regions, where the vegetation is not water-limited, and ii) recent land-use changes (deforestation) that break the natural response in the hydroclimate-vegetation system. These findings provide new evidence on the impacts of the land cover changes on the natural relationship between vegetation and hydroclimatic variability, which is particularly relevant given the increasing rates of deforestation in this region during the recent years.

Australasian (AA) tektites are distal ejecta of a cosmic impact into terrestrial sediments 788.1 ± 2.8 ka. Protracted explorations within the strewn field, as preferred by consensus opinion, have yielded neither an astrobleme nor a proximal imprint. In 3 lesser strewn fields correlated with progenitor astroblemes, tektites are strewn asymmetrically and their total masses and minimum loft distances scale with projectile kinetic energy (KE) partitioning yield. Pursuing an a priori astrobleme location within the uniquely expansive AA strewn field ignores such findings. Absent identification of proximal ejecta in the strewn field, workers are now inferring that indochinite tektites are proximal, dismissing their known devolatilization, weightless vacuum quench and their carefully derived re-entry speeds, ≥ 80% of Earth escape. A defendable guess 40 years ago, but promoting an a priori astrobleme in Indochina is now impeding progress. Ironically, a cosmic link to the Carolina bays’ genesis is considered soundly falsified by the same absence of a correlated astrobleme. We have measured ~50,000 of these shallow, oriented, ovoid basins, located around an annulus focused on Saginaw Bay, Michigan. We posit the ovoid planforms to be surficial manifestations of cavitation voids within an incomprehensible geophysical mass flow of volatiles and entrained target clastics. Unifying both missing astroblemes, we propose an incomprehensible cosmic event on a hemisphere diametrically opposed to the AA distal tektite strewn field. We invoke a highly oblique, perhaps tangential, hypervelocity projectile ricocheting off the Earth’s limb along an extended footprint. Sub-horizontal shock to thick MIS 20 ice sheet overburden triggered endogenic comminution, as stored pressure potential within the substrate was released by phase change of pore water to steam, provisioning fluidized medial ejecta outflow for Carolina bay emplacement. Shocked ice plume expansion augmented tektite velocities, and dissipated significant partitioned KE, preventing another Chicxulub-style global conflagration. The KE partitioning process conspired with intervening ice age transgressions to dislocate proximal ejecta and obfuscate the cosmic signature. AA tektite Suborbital Analysis with appropriate dynamical accounting supports a putative antipodal Saginaw impact site, as does a recent EIGEN 6C4 gravity field assessment. The hypothesis would be falsified if 26Al/10Be burial dating of terraces under Carolina bays disallows bay deposition circa 788 ka.

This research investigated the potential use and application of radar, satellite, and other tracking data for sea ice and weather conditions in maritime-related Search and Research (SAR) operations in the Arctic. Specifically, this study analyzed a SAR event for a missing small vessel due into Utqiaġvik (formerly, Barrow), Alaska in July 2017 as well as archival records of U.S. Coast Guard SAR incidents in Arctic Alaska between 1976 and present. This study feeds into the Arctic Domain Awareness Center (ADAC) funded project - Developing sea ice and weather forecasting tools to improve situational awareness and crisis response in the Arctic - which seeks to create a prototype early warning sea ice and weather forecasting module for hazard planning in Utqiaġvik. This research found that data availability and accessibility, particularly in low bandwidth and further offshore areas, are challenges to data uptake during a SAR event. Nonetheless, the specific SAR incident in Utqiaġvik helped to illuminate there is a breadth of tools that can be applied and used in a SAR context - traditional and knowledge, modeling data, and USCG operational data. Specifically, modeling data from tools developed by the Arctic Domain Awareness Center (ADAC), the University of Alaska Fairbanks (UAF) and other research institutions was generated during this event to help support the local SAR effort. However, a level of pre- or post-processing was necessary in many cases, which can be a challenge for when data is needed immediately. This research holds implications for future use and uptake of modeling data in local SAR operations in Arctic Alaska and potentially the Arctic overall. Given that local SAR operators are predominantly the first line of response to maritime emergencies in Arctic Alaska, the ability to share and provide a set of resources to support SAR operators can be beneficial, particularly in a rapidly changing Arctic. A more targeted and systematic way to utilize and draw upon scientific research for SAR operations can potentially support the local SAR community, especially when immediate information is necessary. In particular, leveraging different products to validate, interpolate, and extrapolate information against one another, can help create more comprehensive situational awareness, especially for further offshore SAR events.

The eWaterCycle platform(https://www.ewatercycle.org/) is a fully Open Source system designed explicitly to advance the state of Open and FAIR Hydrological modelling. Reproducibility is a key ingredient of FAIR, and one of the driving principles of eWaterCycle. While working with Hydrologists to create a fully Open and FAIR comparison study, we noticed that many ad-hoc tools and scripts are used to create input (forcing, parameters) for a hydrological model from the source datasets such as climate reanalysis and land-use data. To make this part of the modelling process better reproducible and more transparent we have created a common forcing input processing pipeline based on an existing climate model analysis tool: ESMValTool (https://www.esmvaltool.org/). Using ESMValTool the eWaterCycle platform can perform commonly required pre-processing steps such as cropping, re-gridding, and variable derivation in a standardized manner. If needed, it also allows for custom steps for a Hydrological model. Our pre-processing pipeline directly supports commonly used datasets such as ERA-5, ERA-Interim, and CMIP climate model data, and creates ready-to-run forcing data for a number of Hydrological models. Besides creating forcing data, the eWaterCycle platform allows scientists to run Hydrological models in a standardized way using Jupyter notebooks, wrapping the models inside a container environment, and interfacing to these using BMI, the Basic Model Interface (https://bmi.readthedocs.io/). The container environment (based on Docker) stores the entire software stack, including the operating system and libraries, in such a way that a model run can be reproduced using an identical software environment on any other computer. The reproducible processing of forcing and a reproducible software environment are important steps towards our goal of fully reproducible, Open, and FAIR Hydrological modelling. Ultimately, we hope to make it possible to fully reproduce a Hydrological model experiment from data pre-processing to analysis, using only a few clicks.

A combination of magmatic and tectonic processes occur on the western branch of the East African Rift System (EARS) driven by active volcanoes adjacent to active rift faults. Mt. Nyiragongo and Mt. Nyamuragira have the most recent eruptive histories of the 8 volcanoes in the Virunga Volcanic Zone (VVZ) located in a region between Rwanda, Uganda and Democratic Republic of Congo (DRC). On May 22nd 2021, Mt.Nyiragongo erupted the first major eruption following its 2002 eruption. This eruption didn’t have the common precursory seismic activity expected before an eruption as was observed in the 2002 eruption seismic record. Rather, there were numerous post-eruption earthquake events with the largest of those events being a magnitude ML 5.1. Around the region of the earthquake swarm, there was observable ground deformation in the city of Goma and Rubavu where surface fissures destroyed houses and split roads apart. This deformation appears to be related to a N-S striking dike intrusion from the volcano trending south towards and under Lake Kivu, according to observed seismicity. In collaboration with the Government of Rwanda, following the May Mt. Nyiragongo eruption, we established a network of 6 seismometers (2 Meridian Compact PH and 4 Trillium Compact PH) operating at 100 sps and two complimentary raspberry Shake and Booms (SBS) around Lake Kivu. This study will focus on characterizing deformation associated with the eruption and the subsequent seismic swarm. Here we present model results based on deformation during the May 2021 eruption as recorded through ALOS InSAR scenes to understand slip concentration during the dike intrusion. Using GTDef, a set of algorithms developed in MATLAB that can incorporate a wide range of geodetic data types to model deformation observed on the Earth’s surface, we model the slip distribution in this region based on the current hypothesis that the observed seismicity was a result of a dike intrusion defined by the southward propagation of the seismic swarm from Mt. Nyiragongo. Given an approximate source, we determine a preferred GNSS/GPS network design based on resolution-cost of additional stations at given locations and discuss first order characterization of the observed deformation.

We present the first Northern Hemisphere high-resolution Holocene methane (CH4) record obtained from the Renland Ice Cap (ReCAP) – 71.3N 26.6W 2300 m a.s.l. in 2015. The total length of the core is 584 m containing 532 m of the Holocene ice without brittle ice zone thus allowing to obtain an uninterrupted CH4 record over the Holocene. An ice core analytical technique developed in 2010 – continuous flow analysis (CFA) provides a unique opportunity of revealing high-resolution greenhouse gas record of the past. Considering a mean annual layer thickness the depth resolution translates to a nominal temporal resolution of 35 data points 100 yr-1 over the Meghalayan (back to 4.2 ka BP) Holocene section. Note that due to the average residence time of CH4 in the atmosphere and the gas age distribution width in the firn, the maximum temporal effective resolution is lower by a factor of 10. The pattern of the centennial scale variability seen in the ReCAP CH4 record is coherent with the earlier published Southern Hemisphere record based on the West Antarctic Ice Divide (WAIS) CH4 record. The wavelet coherence analysis identifies a high correlation on the long- and midterm variability (600-700, 200-500 yr periodicity) as expected; the Meghalayan Holocene section reveals a common variability down to 70 yr. Inter-laboratory offsets of the absolute CH4 values are likely constant as the entire cores were running over continuing measurement campaigns. The elevated ReCAP CH4 level due to the local dust presence is disproved for at least the Last Glacial section. Gas trapping uncertainties should not matter on a decadal scale besides probable layered bubble trapping. Melt layers are untraceable prior to 2 ka BP due to the annual layers thinning. The analysis was performed on the ReCAP record cleaned from the possible melting- and the CFA technique-related spikes. This study leads, however, to a more detailed evaluation of the interpolar difference in the future work as the absolute value remains unresolved though we are confident that there were no big variations. We argue that the centennial variability in the CH4 is explained by the intertropical convergence zone global teleconnection and its influence on the monsoon activity and thus the CH4 production by tropical wetlands. The duration of the periods in CH4 concentration evolves through the time, which could potentially suggest a change in the atmospheric residence time of CH4.

The study and investigation of temperature trends is quite essential for a country like India, whose economy is largely dependent on agriculture as temperature greatly influences rainfall and evapotranspiration processes. Temperature along with other meteorological parameters largely affect the water availability over a region. Almost 83% water is used in agriculture, 12% is consumed by industries and merely 5% is used for domestic purposes. In the present study, long term trend with temperature data of 64 years (1951-2014) was analyzed for maximum, minimum and mean temperature values. After removal of all significant correlation in the data, Mann-Kendall test (MK) was applied. To identify the trends Sen’s slope estimator was used and the overall percent change for the temperature over the study area was determined. To interpolate the spatial pattern an inverse distance weighted interpolation method (IDW) was used using ArcGIS 10.2.2. The analysis showed a significant increase in the trend of temperature for annual and seasonal series during the entire period over the study area i.e. Kharun watershed, Chhattisgarh, India. These results will be quite helpful and play an important role in managing water resources in the basin and will be useful for future planning of irrigation and agricultural practices.