The Ganga plains represent the abode of more than 400 million people and a region of severe anthropogenic disturbance to natural processes. Changing agricultural practices, inefficient use of water, contamination of groundwater systems, and decrease in soil fertility are some of the issues that have affected the long-term resilience of hydrological processes. The quantification of these processes demands a network of hydro-meteorological instrumentation, low-cost sensors, continuous engagement of stakeholders and real time data transmission at a fine interval. We have therefore set up a Critical Zone Observatory (CZO) in a small watershed (21 square Km ) that forms an intensively managed rural landscape consisting of 92% of agricultural land in the Pandu River Basin (a small tributary of the Ganga River). Apart from setting up a hydrometeorological observatory, the major science questions we want to address relate to development of water balance model, understanding the soil-water interaction and estimation of nutrient fluxes in the watershed. This observatory currently has various types of sensors that are divided into three categories: (a) spatially not dense but temporally fine data, (b) spatially dense but temporally not fine data and(c) spatially dense and temporally fine data. The first category represent high cost sensors namely automatic weather stations that are deployed at two locations and provide data at 15 minute interval. The second category includes portable soil moisture, discharge and groundwater level at weekly/ biweekly interval. The third category comprises low-cost sensors including automatic surface and groundwater level sensors installed on open wells to monitor the continuous fluctuation of water level at every 15 minutes. In addition to involving the local communities in data collection (e.g. manual rainfall measurement, water and soil sampling), this CZO also aims to provide relevant information to them for improving their sustainability. The preliminary results show significant heterogeneity in soil type, cropping system, fertilizer application, water quality, irrigation source etc. within a small catchment.

Natural methane gas release from the seafloor is a widespread phenomenon that occurs at cold seeps along most continental margins. Since their discovery in the early 1980s, seeps have been the focus of intensive research, partly aimed to refine the global carbon budget. However, deep-sea research is challenging and expensive and, to date, few works have successfully monitored the variability of methane gas release over long time periods (> 1 yr). Long-term monitoring is necessary to study the mechanisms that control seabed gas release. The M³ project, funded by the German Ministry of Education and Research, aims to study the temporal and spatial variability of gas emissions at the Southern Hydrate Ridge (SHR) by acoustically monitoring and quantifying gas effluxes over several years. Located 850 m deep on the Cascadia accretionary prism offshore Oregon, the SHR is one of the most studied seep sites and persistent but variable gas release has been observed for more than 20 years. Since 2015, the Ocean Observatories Initiative’s (OOI) Cabled Array observatory, provides power supply and two-way communication to the SHR, making it an ideal site for continuous long-term monitoring work. In this work, we present how we will take advantage of the OOI infrastructure and deploy several instruments on the seabed for at least 1.5 year. A multi-beam “overview” sonar mounted on a rotor will identify every gas bubble stream located within 200 m from the sonar location. A scanning “quantification” sonar will be used to estimate the amount of gas that is released from discrete gas streams. A camera system and a CTD probe will help process and analyze the hydro-acoustic data. All instruments will be powered and controlled from land through the OOI infrastructure. We present the instrument design, the operation protocol, as well as the data processing steps and expected results.

The net surface water flux (evaporation minus precipitation minus runoff, E-P-R) of the Atlantic Ocean is approximately 0.4 – 0.6 Sv (1 Sv = 10^9 kg s-1) larger than that of the Pacific Ocean, as shown in atmospheric and oceanic reanalyses and by oceanographic estimates. This asymmetry is linked to the asymmetry in sea surface salinity and the existence of the Atlantic Meridional Overturning Circulation. It is shown that the reason for the asymmetry in E-P-R is greater precipitation per unit area over the Pacific south of 30N, while evaporation rates are similar over both basins. It is further argued that the Pacific Ocean is anomalous compared to the Atlantic and Indian Oceans in terms of atmospheric moisture flux convergence and precipitation across the tropics and subtropics. To clarify the mechanism by which water vapour is exported out of the Atlantic basin and imported into the Pacific, we use an air mass trajectory model driven by ERA-Interim reanalysis. Using 12-hourly releases of 14-day back trajectories on the boundaries of ocean drainage basins over the period 2010-2014, we are able to partition the atmospheric moisture fluxes between basins according to their origins (i.e. last contact with the boundary layer). We show that at most a quarter of the E-P-R asymmetry is explained by higher moisture export to the Arctic and Southern basins from the Atlantic than from the Pacific. The main contributions come from differences in the longitudinal atmospheric transport of moisture between the Atlantic, Indian and Pacific basins. In particular, during the Asian summer monsoon the recurvature of the low level flow in the Somali Jet results in a much weaker westward moisture transport from the Indian into the Atlantic basin than across Central America (where it is similar to the zonal average) while there is stronger eastward transport from the Indian to Pacific basins. The net effect is stronger moisture convergence into the Pacific, but weaker into the Atlantic. In contrast to previous thinking, the role of the moisture flux across Central America in the asymmetry, albeit significant, is not dominant.

Studies of intraseasonal and annual cycles of meteorological variables, using projections of climate change, are nowadays extremely important to improve regional socio-economic planning for countries. This is particularly true in Costa Rica, as Central America has been identified as a climate change hot spot. Today many of the economic activities in the region, especially those related to agriculture, tourism and hydroelectric power generation are linked to the seasonal cycle of precipitation. Changes in rainfall (mm/day) and in the diurnal temperature range ($^{\circ}$C) for the periods 1970-1999 and 2070-2099 were investigated using the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) constructed using the CMIP5 (Coupled Model Intercomparison Project version 5) data. Differences between the multi-model ensembles of the two prospective scenarios (RCP 4.5 and RCP 8.5) and the retrospective baseline scenario were computed. This study highlights Costa Rica as an inflexion point of the climate change in the region and also suggests an early onset of the rainy season and future drying conditions.

The spatio-temporal distribution characteristics of thermospheric mass density have been given more attention with an increasing demand for spacecraft launches and low Earth orbital prediction. More and more patterns of spatial structure and temporal variation are being discovered. Notwithstanding these developments, the study of spatio-temporal coupling in characteristics analysis remains quite limited. In this study, we use a co-clustering method to explore and analyze the spatio-temporal coupling structural characteristics of thermospheric mass density. The processed GOCE satellite dataset is divided into 5 temporal clusters and 20 spatial clusters by the co-clustering method. In terms of spatial structure, the density has an obvious zonal distribution structure and hemispheric asymmetry. Moreover, due to the influence of the Earth’s magnetic field, there is an average angle about 2.00° between the band structure and the latitudinal circle. In terms of temporal structure, the temporal patterns of density can be grouped into five period types, namely the quiet period, the moderate activity period, the event period, the oscillation period and the recovery period. And significant positive correlation can be found between the F10.7 indices and the temporal density variation. This study explores the spatial structure and temporal pattern of thermospheric mass density and its driving forces from the perspective of spatio-temporal coupling based on a statistical method, which can provide a new idea of spatio-temporal coupling method for spatio-temporal evolution of thermospheric mass density.

A document by Saira Hamid. Click on the document to view its contents.

The uncontrolled rapid population growth in our regions and strong industrialization are putting pressure on natural resources, accelerating climate change and desertification. This study aims to follow the evolution of land use in the N’ZI watershed. Three images from Landsat 4 & 5 (1986), Landsat 7 (2000), and Landsat 8 (2020) made it possible to carry out this study. Remote sensing and geographic information systems (GIS) have been used to monitor land cover as a whole. The software Envi 5.1 and ArcGIS 10.4.1 have made it possible to do various treatments. The supervised classification method was used in this work in addition to the calculation of the spectral indices. The land-use analysis showed the changes that took place during the periods 1986-2000, 2000-2020, and 1986-2020. The results of this analysis showed regression of water surfaces (-64.95% and-52.47%) over the period (2000-2020 and 1986-2020) on the other hand, there is a great increase in bare-ground dwellings (373.63%) and low-cover soils (10.60%). These progressions are at the expense in particular of forests (-86.93%), savannas (-3.97%), and agricultural areas (-9.30%) between 1986-2020.

The Amazon River has the largest volume on earth, making up 15–20% of the annual fluvial discharge into oceans. The neighboring Pará River mixes with the Amazon River waters in the Amazon Estuary before forming a plume that extends into the Atlantic. Despite the global importance of these rivers, dissolved trace metal fluxes from this estuary remain unknown. Here we present data for dissolved (<0.2 µm) trace metals (Al, Mn, Fe, Co, Ni, Cu, Zn, Cd, Pb and U) in the Amazon Estuary during the high discharge season (April–May 2018). We observed distinct trace metal signatures for the Amazon and Pará Rivers, reflecting different catchment areas. Concentrations of the particle-reactive elements (Mn, Fe and Pb) decreased rapidly at low-salinity (S≤2), resulting in the highest estuarine removal (86–94% in the Amazon; 61–70% for the Pará). Co, Ni and Cu removal was comparatively low in both river transects (6–39%), while Cd was the only element with a consistent net input. Chemical fluxes were estimated using (a) endmember concentrations and estuarine removal and (b) combining trace element concentrations with 228Ra fluxes. Relative to global total river fluxes, the Amazon and Pará Rivers combined contribute 21% of dissolved Cu and 18% of dissolved Ni during the high discharge season, but account for comparatively low fractions of Mn, Fe, Co and Zn. These data quantify, for the first time, the trace metal output from the world’s largest and 5th largest river into the Atlantic Ocean, filling a critical gap in knowledge of this globally-important region.

Highlights: • Evidence for a 175 km thick lithosphere beneath the Archean Eastern Dharwar craton. • The lithosphere has a shear wave velocity of 4.7-4.8 km/s, typical for a craton. • Moderate coupling between the Dharwar craton lithosphere and asthenosphere. • Collocated seismological and kimberlite xenolith data reveal undisturbed craton root.

AbstractFeature interaction is a crucial concept when it comes to machine learning interpretability. These interactions make it easier for the person using a machine learning tool to know how the model made its prediction. Intrinsic models such as the Generalized linear models pull their interpretability aspects from detecting feature interactions on the data. However, these models do not search the whole sample space for interactions and assume all interactions to the target feature are identical. A hybrid model combining Rough Set and Generalized linear models is proposed. Rough Set uses the concept of information granulation and approximations of regions with meaningful information to find feature interactions in the whole sample space. The detected features will then be modeled onto a Generalized linear model for prediction purposes. The dataset used in the experiment was drawn from wunderground.com online weather site, specifically the Kariki Farm online weather station. The proposed methodology for the research is the CRISP-DM method.Keywords: Machine interpretability, Rough Set, Generalized linear model, weather prediction.

Media propagation delay and delay-rate induced by the water vapor within the Earth's troposphere represent one of the main error sources for radiometric measurements in deep space. In preparation for the BepiColombo and JUICE missions, the European Space Agency has installed and operates the prototype of a tropospheric delay calibration system (TDCS) at the DSA-3 ground station located in Malargüe, Argentina.An initial characterization of the TDCS performance was realized using two-way Doppler measurements at X-band to perform the orbit determination of the Gaia spacecraft. This work will further characterize the system by analyzing two-way Doppler and range data at X- and Ka-band for 31 tracking passes of the BepiColombo spacecraft, which were recorded between March 2021 and February 2022 during the first two solar conjunction experiments. The performance exceeds the expectations based on the previous analysis, with a reduction of the Doppler noise of 51% on average and up to 73% when using the TDCS measurements in place of standard calibrations based on global navigation satellite system data. Furthermore, the campaign serves as validation of the TDCS operations during superior solar conjunctions, with most of the tracking passes at low elongation now satisfying the Mercury orbiter radioscience experiment requirements on two-way Doppler stability. These results, which are in line with those of similar instruments installed at other Deep Space Network antennas, are obtained using a commercial microwave radiometer with significantly lower installation and maintenance costs.

The Solar Dynamics Observatory (SDO) is a solar mission in an inclined geosynchronous orbit. Since commissioning, images acquired by Atmospheric Imaging Assembly (AIA) instrument on-board the SDO have frequently displayed “spikes”, pixel regions yielding extreme number of digital counts. These are theorized to occur from energetic electron collisions with the instrument detector system. These spikes are regularly removed from AIA Level 1.0 images to produce clean and reliable data. A study of historical data has found over 100 trillion spikes in the past decade. This project correlates spike detection frequency with radiation environment parameters in order to generate an augmented data product from SDO. We conduct a correlation study between SDO/AIA data and radiation belt activity within the SDO’s orbit. By extracting radiation “spike” data from the SDO/AIA images, we produce a comprehensive data product which is correlated not only with geomagnetic parameters such as Kp, Ap and Sym-H but also with the electron and proton fluxes measured by the GOES-14 satellite. As a result, we find that AIA spikes are highly correlated with the GOES-14 electrons detected by the MAGED and EPEAD instruments at the equator (where the two satellites meet) with Spearman’s Correlation values of ρ=0.73 and ρ=0.53 respectively, while a weaker correlation of ρ=0.47 is shown with MAGPD protons for the two year period where both missions returned data uninterruptedly. This correlation proves that the SDO spike data can be proven useful for characterizing the Van Allen radiation belt, especially at areas where other satellites cannot.

We describe new publicly-available, multi-year formaldehyde (HCHO) data records from the Ozone Mapping and Profiler Suite (OMPS) nadir mapper (NM) instruments on the Suomi NPP and NOAA-20 satellites. The OMPS-NM instruments measure backscattered UV light over the globe once per day, with spatial resolutions close to nadir of 50 × 50 km² (OMPS/Suomi-NPP) and 17 × 17 km² or 12 × 17 km² (OMPS/NOAA-20). After a preliminary instrument line shape and wavelength calibration using on-orbit observations, we use the backscatter measurements in a direct spectral fit of radiances, in combination with a nadir reference spectrum collected over a clean area, to determine slant columns of HCHO. The slant columns are converted to vertical columns using air mass factors derived through scene-by-scene radiative transfer calculations. Finally, a correction is applied to account for background HCHO in the reference spectrum, as well as any remaining high-latitude biases. We investigate the consistency of the OMPS products from Suomi NPP and NOAA-20 using long-term monthly means over 12 geographic regions, and also compare the products with publicly-available TROPOMI HCHO observations. OMPS/Suomi-NPP and OMPS/NOAA-20 monthly mean HCHO vertical columns are highly consistent (r = 0.98), with low proportional (2 %) and offset (2×10¹⁴ molecules cm⁻²) biases. OMPS HCHO monthly means are also well-correlated with those from TROPOMI (r = 0.92), although they are consistently 10±16 % larger in polluted regions (columns >8×10¹⁵ molecules cm⁻²). These differences result primarily from differences in air mass factors.

The 2019 Museum Fire burned in a mountainous region near the city of Flagstaff, AZ, USA. Due to the high risk of post-wildfire debris flows and flooding entering the city, we deployed a network of seismometers within the burn area and downstream drainages to examine the efficacy of seismic monitoring for post-fire flows. Seismic instruments were deployed during the 2019, 2020, and 2021 monsoon seasons following the fire and recorded several debris flow and flood events, as well as signals associated with rainfall, lighting and wind. Signal power, frequency content, and wave polarization were measured for multiple events and compared to rain gauge records and images recorded by cameras installed in the study area. We use these data to demonstrate the efficacy of seismic recordings to (1) detect and differentiate between different energy sources, (2) estimate the timing of lightning strikes, (3) calculate rainfall intensities, and (4) determine debris flow timing, size, velocity, and location. This work confirms the validity of theoretical models for interpreting seismic signals associated with debris flows and rainfall in post-wildfire settings and demonstrates the efficacy of seismic data for identifying and characterizing debris flows.

A supermodel connects different models interactively so that their systematic errors compensate and achieve a model with superior performance. It differs from the standard non-interactive multi-model ensembles (NI), which combines model outputs a-posteriori. We formulate the first supermodel framework for Earth System Models (ESMs) and use data assimilation to synchronise models. The ocean of three ESMs is synchronised every month by assimilating pseudo sea surface temperature (SST) observations generated from them. Discrepancies in grid and resolution are handled by constructing the synthetic pseudo-observations on a common grid. We compare the performance of two supermodel approaches to that of the NI for 1980—2006. In the first (EW), the models are connected to the equal-weight multi-model mean, while in the second (SINGLE), they are connected to a single model. Both versions achieve synchronisation in locations where the ocean drives the climate variability. The time variability of the supermodel multi-model mean SST is reduced compared to the observed variability; most where synchronisation is not achieved and is bounded by NI. The damping is larger in EW than in SINGLE because EW yields additional damping of the variability in the individual models. Hence, under partial synchronisation, the part of variability that is not synchronised gets damped in the multi-model average pseudo-observations, causing a deflation during the assimilation. The SST bias in individual models of EW is reduced compared to that of NI, and so is its multi-model mean in the synchronised regions. The performance of a trained supermodel remains to be tested.

The mineral dolomite (CaMg(CO3)2) forms in only small quantities in modern oceans, cannot be precipitated abiotically from unmodified seawater in laboratory experiments, yet comprises much of the carbonate rock record. The challenge of explaining the apparent temporal discrepancy in dolomite, the “dolomite problem,” has fascinated carbonate sedimentologists for centuries. Yet, this pursuit has lacked a quantitative tabulation of dolomite in the rock record. Here, we use the North American rock record, as archived in Macrostrat, to assemble a record of dolomite abundance through geologic time. The completeness and age resolution of our dataset allow us to compare dolomite abundance with environmental variables, including stromatolite abundance, evaporite occurrences, sea level, glaciation, and temperature. We use these comparisons to test the assumption that the bulk of the geologic dolomite record was formed via secondary diagenetic processes. We find no monotonic decrease in abundance with age-the expected result if late diagenesis affects the bulk of the record. Dolomite was just as abundant during the first half of the Paleozoic as it was during most of the Neoproterozoic, a challenge to canonical thinking. We show that a number of dolomite precipitation mechanisms known from modern environments and experimentally grown dolomite can explain many of the patterns we observe in the North American dolomite record. Perhaps dolomite is not such a problem after all.

It is widely acknowledged that distributed water systems (DWSs), which integrate distributed water supply and treatment with existing centralized infrastructure, can mitigate challenges to water security from extreme events, climate change, and aged infrastructure. However, there is a knowledge gap in finding beneficial DWS configurations, i.e., where and at what scale to implement distributed water supply. We develop a meso-scale representation model that approximates DWSs with reduced backbone networks, which enable efficient system emulation while preserving key physical realism. Moreover, system emulation allows us to build a multi-objective optimization model for computational policy search that addresses energy utilization and economic impacts. We demonstrate our models on a hypothetical DWS with distributed direct potable reuse (DPR) based on the City of Houston’s water and wastewater infrastructure. The backbone DWS with greater than 92% link and node reductions achieves satisfactory approximation of global flows and water pressures, to enable configuration optimization analysis. Results from the optimization model reveal case-specific as well as general opportunities, constraints, and their interactions for DPR allocation. Implementing DPR can be beneficial in areas with high energy intensities of water distribution, considerable local water demands, and commensurate wastewater reuse capacities. The meso-scale modeling approach and the multi-objective optimization model developed in this study can serve as practical decision-support tools for stakeholders to search for alternative DWS options in urban settings.

We present partial ring distributions of solar wind protons observed by the Rosetta spacecraft at comet 67P/Churyumov-Gerasimenko. The formation of ring distributions is usually associated with high activity comets, where the spatial scales are larger than multiple ion gyroradii. Our observations are made at a low-activity comet at a heliocentric distance of 2.8 AU on April 19th, 2016, and the partial rings occur at a spatial scale comparable to the ion gyroradius. We use a new visualisation method to simultaneously show the angular distribution of median energy and differential flux. A fitting procedure extracts the bulk speed of the solar wind protons, separated into components parallel and perpendicular to the gyration plane, as well as the gyration velocity. The results are compared with models and put into context of the global comet environment. We find that the formation mechanism of these partial rings of solar wind protons is entirely different from the well-known partial rings of cometary pickup ions at high-activity comets. A density enhancement layer of solar wind protons around the comet is a focal point for proton trajectories originating from different regions of the upstream solar wind. If the spacecraft location coincides with this density enhancement layer, the different trajectories are observed as an energy-angle dispersion and manifest as partial rings in velocity space.