bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the riverine flood forecast skill of GloFAS with streamflow observations and impact data: a case study for Mali
Authors: van den Homberg Marc , Ficchi Andrea , Phung Phuoc , Sangare Sidiky , Gado Djibo Abdouramane , Kane Cheikh
Published: March 2022

Riverine floods are one of Mali's most devastating and frequently occurring disasters. However, so far, actions linked to it are mainly post-disaster ones. For this reason, the Mali Red Cross has recently established with partners a Forecast-based Financing mechanism that triggers early actions to reduce the impacts of floods once a predefined trigger is reached. Given the lack of forecasts at the national scale, the current trigger model is based only on real-time observations from the hydrological monitoring network of the National Directorate of Hydraulics (DNH): if the observed upstream water level exceeds the 5-year return period, an action is triggered to prepare for floods downstream, four days ahead, taking into account the delays in the propagation of the flood. Global flood forecasting systems can possibly complement this local flood monitoring model, especially in large transboundary river basins. This research aims to investigate the riverine flood forecast skill of the Global Flood Awareness System (GloFAS version 3.1, part of the Copernicus Emergency Management Service) in the Niger river basin by evaluating reforecast data against two reference datasets: river flow observations and impact data. The False Alarm Ratio (FAR) and the Probability of Detection (POD) have been calculated for all available extended-range reforecasts (lead times up to 46 days) over a 20-year period and for 15 river gauge station locations. For the skill assessment of GloFAS against river flow observations, most river gauge stations with enough observed data (8 out of 15) show good and robust skill scores for all lead times up to 10 days. For the skill assessment based on impact data, even though at some stations the POD is good, the FAR is too high. A preliminary conclusion is that setting trigger levels for longer lead times (up to 10 days) - to complement the existing monitoring system with a four-day lead time - can be done only for those locations where enough historical observed data is available. Using impact data to set triggers is currently hampered by limitations of the impact dataset, such as no precise event dates and locations.
Simulation of a heavy rainfall-induced landslide event over Kulonprogo, Yogyakarta in Indonesia using WRF: Sensitivity to cloud microphysics parameterization
Authors: Nuryanto Danang Eko , Satyaningsih Ratna , Nuraini Tri Astuti , Sopaheluwakan Ardhasena , Ettema Janneke , Jetten Victor G , Permana Donaldi Sukma , Riama Nelly Florida
Published: March 2022

In this study, we used the Weather Research and Forecasting (WRF) version 4.2.1 model to simulate the characteristics of a rainfall-induced landslide that occurred on November 28 in Samigaluh, Kulonprogo. In addition, we investigated 22 different microphysics (MP) schemes to see how sensitive they were. The WRF model was employed with three nested domains, the innermost of which had a 1 km grid spacing and explicit convection. The model was run for 73 hours with GFS initial conditions from 00:00 UTC on November 26, 2018. We used reflectivity profiles from the Weather Radar in Yogyakarta and data from rain gauge stations in Kulonprogo to validate the simulated properties of the rainfall. Despite employing identical initial and boundary conditions and model settings, the MP approaches have significant variances in their thunderstorm simulations. To begin with, practically all of the extreme convection simulation methods over Samigaluh had the same pattern as the reported storm. For example, on November 27, radar data indicated the passage of three convective cores above Samigaluh, which the model in most MP schemes simulated. In comparison, the Ferrier_old scheme did an excellent job of simulating the convective cores' observable features. The MP schemes also had difficulties modeling the storm's updrafts. The Ferrier old scheme simulated surface rainfall distributions closer to data than the other three schemes (Goddard GCE, Morrison2, and WDM5). On the other hand, all four MP systems did an excellent job of simulating the convective variations associated with the thunderstorm. The model's generated reflectivity profiles, which showed three convective cores, were similar to the observed reflectivity profile. These characteristics match the simulated convective profiles, which peaked between 10 and 15 kilometers. The current research reveals that the microphysical systems in thunderstorm simulations have a lot of sensitivity and variability. The study also underlines the necessity for a multi-observational program such as Year of Maritime Continent (YMC) to improve the parameterization of cloud microphysics and land surface processes throughout the Indonesian region. 
Twenty years of volume transport from satellite gravimetry in the Atlantic and Southern Ocean
Authors: Kvas Andreas , Bentel Katrin , Behzadpour Saniya , Mayer-Gürr Torsten
Published: March 2022

With an observation period of almost twenty years and global data coverage, satellite gravimetry has become a crucial tool for monitoring the state of our planet in a changing climate. Gravimetry-derived mass change has seen numerous applications in different geoscientific disciplines and has fundamentally improved our understanding of the Earth system. One such application is the determination of meridional and zonal volume transport variability based on ocean bottom pressure (OBP) variations, which can provide key insights into climate-relevant ocean currents like the Atlantic Meridional Overturning Circulation (AMOC) or the Antarctic Circumpolar Current (ACC). However, the limited spatial resolution, signal leakage from other geophysical subsystems like the hydrosphere, cryosphere or solid Earth make satellite gravimetry-derived transport estimates difficult to interpret. In this study we investigate geostrophic volume transport variability based on observations of the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) for selected cross sections in the Atlantic and Southern Ocean. We focus on interannual transport variations in the deep ocean, where the more moderately sloping topography poses less stringent requirements on the spatial resolution of the OBP fields, and the lower temporal resolution reduces the impact of observation noise by providing longer averaging periods. Basis for the derived transport variations are high-resolution OBP fields determined in an ensemble Kalman filter approach. This allows us to also propagate the inherent observational noise to transport level and together with glacial isostatic adjustment (GIA) und hydrological model statistics quantify the uncertainty and sensitivity of the derived transport time series. We further contrast results for the Atlantic and Southern Ocean and show the different impact of the satellite observation geometry on meridional and zonal transport estimates.
Early warning signals for topological tipping points
Authors: Charó Gisela Daniela , Ghil Michael , Sciamarella Denisse
Published: March 2022

The topology of the branched manifold associated with the Lorenz model’s random attractor (LORA) evolves in time. LORA’s time-evolving branched manifold robustly supports the point cloud associated with the system’s invariant measure at each instant in time. 

This manifold undergoes not only continuous deformations — with branches that bend, stretch or compress — but also discontinuous deformations, with branches that intersect at discrete times. These discontinuities in the system's invariant measure manifest themselves in the decrease or increase of the number of 1-holes, thus producing abrupt changes in the branched manifold’s topology.

Topological tipping points (TTPs) are defined as abrupt changes in the topology of a random attractor’s branched manifold. Branched Manifold Analysis through Homologies
(BraMAH) is a robust method that allows one to detect these fundamental changes. 
The existence of such TTPs is being confirmed by careful statistical analysis of LORA’s time-evolving branched manifold, following up on Charó et al. (Chaos, 2021, doi:10.1063/5.0059461). Research is being pursued on early warning signals for these TTPs, concentrating on local fluctuations in the system’s invariant measure.
Effects of transported plant on soil physical properties
Authors: Tian Jin , Xu Xiangzhou
Published: March 2022

Vegetation has long been recognized as an efficient way of preventing soil erosion. However, the effects of plants on gravity erosion are still broadly unknown. This study conducted a plant-root modelling experiment with the Pinus tabuliformis and Amorpha fruticosa under heavy rainfall on a micro-plot with a slope of 4° in the Wangdong Village, Shaanxi Province. The results showed that: (1) As the depth of the soil increases, the soil becomes more compacted and the influence of tree and shrub roots on soil consolidation ability increases. In the shallow soil with the depth of 20 cm , the maximum soil shear strength was 17 kpa and the maximum soil hardness was 21 N/cm2 next to Pinus tabuliformis, while in the deep soil with the depth of 40 cm, the maximum soil shear strength was 22 kpa and the maximum soil hardness was 44 N/cm2. In the shallow soil, the maximum soil shear strength was 16 kpa and the maximum soil hardness was 20 N/cm2 next to Amorpha fruticosa, while in the deep soil the maximum soil shear strength was 19 kpa and the maximum soil hardness was 48 N/cm2. (2) The differences of the root types make the plants differ in their soil consolidation abilities, for the tree root have a stronger consolidation ability than shrub roots. In the shallow soils, the soil water content next to Pinus tabuliformis was slightly lower than that of Amorpha fruticosa, while in deeper soils they were close to each other, so the soil cohesion next to Pinus tabuliformis was slightly higher than that of soil next to Amorpha fruticose. The maximum soil cohesion next to Pinus tabuliformis was 22 kpa and that of soil next to Amorpha fruticosa was 19 kpa. The maximum soil hardness next to Pinus tabuliformis was 44 N/cm2 and that of soil next to Amorpha fruticosa was 48 N/cm2.
Conceptualization of recharge processes in the world most populous megacity in the past three decades using WetSpass-M Model: lessons and opportunities for Managed Aquifer Recharge (MAR) 
Authors: Olabode Oluwaseun , Comte Jean-Christophe
Published: March 2022

Current demographics have projected that Lagos will become the world’s most populated megacity by the year 2100. This rapidly increasing human population has led to rapid depletion of groundwater resources of its highly productive coastal aquifers. The recharge processes and its drivers are yet to be quantified and understood. This challenge prompt the need to estimate the past and present recharge rate and understand the recharge processes in the megacity using WetSpass-M model. Input data such as land cover, DEM, slope, water depth, soil, temperature, precipitation, evapotranspiration, windspeed representing the past three decades (1990 to 2020) were inputted into the model. The findings established the two contrasting trends with recharge rates decreasing from 761mm/yr to 563mm/yr and runoff increasing from 292mm/yr to 400mm/yr. Similar spatial patterns of land use, simulated recharge and runoff were observed in the central area of Lagos. This supports the pressures from urbanization activities in the reduction of infiltrating water expected to recharge the aquifers and increasing runoff waters with potential of creating environmental hazards. The increasing runoff amount at the places near the water bodies serves as source water for Fiver Bank Filtration takeoff of Managed Aquifer Recharge (MAR) that can be captured, treated and stored in aquifers for later use. This when used will reduce the water availability crisis reported and projected in city of Lagos. 
A new observational-modelling framework for algae bloom monitoring and forecast in the Baltic Sea
Authors: Koszalka Inga Monika , Tachon Foucaut , Karlson Agnes ML
Published: March 2022

Algae blooms, specifically cyanobacterial blooms, are frequent in the Baltic Sea and pose major environmental problems for the marine ecosystem and coastal societies. Surface accumulations of algae exacebate eutrophication, limit access to oxygen and can be toxic to humans and marine life. They affect marine services including drinking water resources, marine operations, tourism and fishing. Monitoring of algae blooms based on satellite-borne and in-situ data have been ongoing for years. However, a proper assessment of monitoring needs to cover complex spatio-temporal variability of the blooms as well as reliable early warning and forecasts systems are still lacking, owing to the complexity of physical and biological processes involved in algae growth and too sparse data to constrain complex marine ecosystem models. As algal blooms are expected to intensify under the observed long-term warming of surface waters, developing relevant monitoring-early warning systems is a priority.

Our interdisciplinary collaboration aims at a tantalizing task of building a framework tailored for monitoring and forecasting of algae blooms in the Baltic Sea. The framework combines surface drift observations, in-situ observations, remotely-sensed chlorophyll products as well as numerical simulations of Lagrangian (drifting) trajectories driven by the ocean state forecast available at the Copernicus Marine Environment Monitoring Service (CMEMS). The first step toward this goal consisted of collecting observations of the surface drift in the Baltic Sea relevant to the dispersion of algae accumulations. To this end, we deployed 6 CARTHE Smart surface drifter platforms in Western Gotland Basin in August 2021. The CARTHE drifter platforms are designed to sample sea currents close to the surface compared to other standard drift measurements, provide a very accurate positioning data at 15 minute intervals, and their floating parts are biodegradable. We will present data from this experiment as well the results from a comparison between the observed surface drift and CMEMS-driven Lagrangian simulations. The results using relative dispersion statistics point to a good skill of the model-driven drift forecast (a few km error in mean dispersion over a two day scale). We extend the analysis including Lagrangian ecosystem modelling, spectral analysis and clustering approaches, taking into the consideration sparseness of in-situ data.
Supervised machine learning to estimate instabilities in chaotic systems: computation of local Lyapunov exponents
Authors: Ayers Daniel , Lau Jack , Amezcua Javier , Carrassi Alberto , Ojha Varun
Published: March 2022

Weather and climate are well known exemplars of chaotic systems exhibiting extreme sensitivity to initial conditions. Initial condition errors are subject to exponential growth on average, but the rate and the characteristic of such growth is highly state dependent. In an ideal setting where the degree of predictability of the system is known in real-time, it may be possible and beneficial to take adaptive measures. For instance a local decrease of predictability may be counteracted by increasing the time- or space-resolution of the model computation or the ensemble size in the context of ensemble-based data assimilation or probabilistic forecasting.

Local Lyapunov exponents (LLEs) describe growth rates along a finite-time section of a system trajectory. This makes the LLEs the ideal quantities to measure the local degree of predictability, yet a main bottleneck for their real-time use in  operational scenarios is the huge computational cost. Calculating LLEs involves computing a long trajectory of the system, propagating perturbations with the tangent linear model, and repeatedly orthogonalising them. We investigate if machine learning (ML) methods can estimate the LLEs based only on information from the system’s solution, thus avoiding the need to evolve perturbations via the tangent linear model. We test the ability of four algorithms (regression tree, multilayer perceptron, convolutional neural network and long short-term memory network) to perform this task in two prototypical low dimensional chaotic dynamical systems. Our results suggest that the accuracy of the ML predictions is highly dependent upon the nature of the distribution of the LLE values in phase space: large prediction errors occur in regions of the attractor where the LLE values are highly non-smooth.  In line with classical dynamical systems studies, the neutral LLE is more difficult to predict. We show that a comparatively simple regression tree can achieve performance that is similar to sophisticated neural networks, and that the success of ML strategies for exploiting the temporal structure of data depends on the system dynamics.
Variability and changes of tropical-extratropical cloudband events over the Southern Hemisphere using a synoptic climatology approach 
Authors: Zilli Marcia , Hart Neil
Published: March 2022

The aridity of the Southern Hemisphere subtropical latitudes is frequently punctuated by near-planetary scale bands of cloud that diagonally link the tropics to the mid-latitudes. These cloud bands preferentially occur in the subtropical convergence zones situated in each of the three ocean basins. However, much of the research focused on these regions has considered seasonal averages, which are not suitable for fully capturing the synoptic-scale variability of these events. The MetBot, first developed to identify cloud bands over southern Africa and now successfully adapted to the South American area, overcomes this problem by identifying these tropical-extratropical cloud bands in daily data. In this study, we use the near-real-time version of the MetBot, currently implemented to monitor the progress of cloudband seasons over the Southern Hemisphere. Cloud bands are identified in the daily outgoing longwave radiation data from the High-Resolution Infrared Radiation Sounder (HIRS), developed by the National Oceanic and Atmospheric Administration (NOAA), with a spatial resolution of 1° and latency time of a couple of days (Lee, 2011). The results of the near-real-time monitoring area are available at . Here, we considered the cloud bands dataset with events identified across the Southern Hemisphere between 1979 and 2020 to evaluate their variability and recent changes. The climatological analysis highlights four hotspot regions: South Pacific and South America, which are the strongest; southern Africa, with events only forming through austral summer; and Australia, with summer (winter) events over the eastern (western) coast. The hemispheric scale of the results provides a unified view of the interannual and intraseasonal variability of the cloud bands, including the preferential location of the convective activity during different ENSO phases. By isolating the systems responsible for most of the rainy season precipitation over the subtropics, we also identify changes in the cloud band activity in recent years and their impact on the total precipitation. Future applications include the use in tandem with S2S forecast data, as currently being implemented over South America, as well as a tool to evaluate historical and future climate model simulations of various resolutions.  

Lee, Hai-Tien and NOAA CDR Program (2011): NOAA Climate Data Record (CDR) of Daily Outgoing Longwave Radiation (OLR), Version 1.2. NOAA National Climatic Data Center. doi:10.7289/V5SJ1HH2 
The increasing role of dry surface condition in recent heatwaves over Indian subcontinents
Authors: Kumar Shashi Gaurav , Goswami Ajanta
Published: March 2022

The impacts of recent heatwaves are of cause for concern since these events are projected to become more intense and frequent in nature and of longer duration. Indian region has faced frequent heatwave spells from 1980 to 2020, and on average, it has recorded more than 18 days with excess heat and has only increased in the last decade. We used Era-5 reanalysis and ground station data to analyze the extreme events. We identify heatwave events and their zone for 1980 to 2020. In most circumstances, the clear sky of April-May-June plays a positive role in driving positive net surface thermal radiation and anomaly. We have observed that the max temperature and anomaly peaked during the dry period of May-June before the arrival of the summer monsoon, suggesting the increasing role of dry surface condition and its feedback in driving extreme heat conditions. The surface sensible heat flux anomaly started developing as the surface lost water content and dried. For most events, it began from mid-April and peaked at mid-June when no or very little water content was left in the soil. Also, during this period, the net radiation at the surface was anomalous within the heatwave zone, leading to a significant increase in surface sensible heat flux anomaly. This development in surface sensible heat flux caused effective local forcing on surface air temperature, leading to increased intensity of the heatwaves. In this study, also, we have observed that the uncultivated and dry land left during May-June can strengthen heatwave under dry surface conditions.
Ground response of seasonal Pc5 power to varying solar wind conditions
Authors: Hynönen Reko , Tanskanen Eija
Published: March 2022

Pc5 waves are a sub-group of ultra-low frequency (ULF) waves in the magnetosphere. We determine the Pc5 wave power from ground magnetometer measurements in IMAGE network and statistically study their dependence on solar wind conditions, like solar wind speed and dynamic pressure, separating them from the solar phase and solar conditions in a statistical sense.

Pc5 power is dependent on the magnetic local time, season, and magnetic latitude. We show that while it is always heavily modulated by solar wind speed, the intensity of its ground response also varies over time. Particularly, the ground response is usually the strongest in the morning and midnight hours, while a minor maximum can sometimes be found in the midday or afternoon hours.
Direct shear experiments to investigate the effect of chemical alteration on fault frictional behaviour in granitic geothermal systems
Authors: Harpers Nick , Forbes Inskip Nathaniel , Allen Michael John , Faulkner Daniel , Claes Hannes , Busch Andreas , den Hartog Sabine
Published: March 2022

Enhanced temperature gradients related to locally elevated heat production in granitic plutons offer the potential for low carbon geothermal energy production. Cornwall in SW England hosts several granitic plutons that are the subject of current geothermal projects (United Downs Deep Geothermal Power [UDDGP] Project and Eden Project). These projects target fault zones in crystalline rock that provide pre-existing pathways for fluid flow. Reinjection of cooler fluids into the reservoir after heat extraction may result in chemical disequilibrium with the host rock, potentially driving precipitation or chemical alteration. Such changes could influence the frictional properties of the fault zones, and hence require modifications to numerical risk-based calculations of the likelihood, or not, of induced seismicity.

In order to study the effects of such alterations, we have conducted a series of direct shear experiments under representative in-situ conditions on Cornish Carnmenellis granite samples which have undergone varying degrees of natural chemical alteration. The direct shear experiments were conducted on gouges (grain size < 125 μm) and at effective normal stresses of 80-105 MPa, pore fluid pressures of 25-50 MPa and temperatures of 16-180 °C. These conditions are relevant for the depths where the UDDGP project injection and production boreholes intercept the Porthtowan Fault zone, the assumed main conduit for fluid flow. In each test, load point velocity was stepped between 0.3 μm/s, 1 μm/s and 3 μm/s, and shear resistance of the sample was measured to determine the stability of sliding and thus the likelihood of induced seismicity as a function of degree of alteration. Initial shear tests at room temperature suggest little difference in the frictional response of altered and unaltered samples.
Leveraging Digital Technology to Improve EDI in Geosciences: GeoCoLab
Authors: Raji Munira , Bond Clare , Carter Elliot , Davies-Vollum Sian , Butcher Ginger , Lewis Edd , Thomas Brian , Williams Rebecca
Published: March 2022

Geoscience research is inequitably distributed within the UK and worldwide due to a lack of access to analytical facilities and associated funding. This disproportionately affects minority and marginalised researchers. Geoscience research relies on access to analytical facilities to create fundamental datasets; however, lack of analytical facilities access negatively affects success and retention in research, impacting diversity in geoscience. Equality, Diversity and Inclusion (EDI) issues in analytical geoscience were investigated through participating in a recent NERC’s Digital Technologies to Open Up Environmental Sciences Digital Sprint hackathon, including an online survey to understand how different groups access analytical facilities globally. Analysis of the survey data revealed a lack of funding to cover analytical costs and prohibitively competitive national schemes as barriers to accessing analytical facilities. The analysis also suggests that a lack of access or perceived lack of access to facilities has stopped 77% of respondents from pursuing an avenue of research, and 71% have switched research topics. To address the access gap, we are developing an app - GeoColab, a digital technology platform to break down barriers in analytical geoscience. The GeoCoLab App funded by the Natural Environment Research Council (NERC) aims to solve the lack of access by ‘match–making’ underserved geoscience researchers who need analytical services, with collaborating laboratory facilities that have agreed to offer a quota of pro-bono services.
Impact of the winter convective event on gelatinous zooplankton in the open southern Adriatic
Authors: Batistić Mirna , Garić Rade , Hure Marijana
Published: March 2022

The southern Adriatic is the deepest part of the Adriatic Sea (1242 m) and one of three sites of open-sea deep convection in the Mediterranean. By analyzing zooplankton samples taken in the open southern Adriatic in winter and spring/summer 2021 we investigated effect of winter vertical mixing on distribution of gelatinous zooplankton. During the convection time in winter, gelatinous zooplankton abundance was low and unusual vertical distribution for some species was occurred. In the spring-summer time an increase in gelatinous zooplankton abundance in upper and deeper layer was registered. This is probably related to the early spring phytoplankton bloom enhanced by nutrient input into euphotic zone due to winter mixing phase. As a consequence of this event, there is also availability of more food for deep-sea gelatinous organisms.

 
Responses of concentration and sources of black carbon in a megacity during the COVID-19 pandemic
Authors: Li Weijun , Xu Liang
Published: March 2022

Black carbon (BC) not only warms the atmosphere but also affects human health. The nationwide lockdown due to the COVID-19 pandemic led to a major reduction in human activity during the past thirty years. Here, the concentration of BC in the urban, urban-industry, suburb, and rural areas of a megacity Hangzhou were monitored using a multi-wavelength Aethalometer to estimate the impact of the COVID-19 lockdown on BC emissions. The citywide BC decreased by 44% from 2.30 μg/m3 to 1.29 μg/m3 following the COVID-19 lockdown period. The source apportionment based on the Aethalometer model shows that vehicle emission reduction responded to BC decline in the urban area and biomass burning in rural areas around the megacity had a regional contribution of BC. We highlight that the emission controls of vehicles in urban areas and biomass burning in rural areas should be more efficient in reducing BC in the megacity Hangzhou.
Estimating the productivity of boreholes in fractured crystalline basement using lineaments extracted from remote sensing data
Authors: Brice Tagne Dzukam , Hermans Thomas , Daniel Hervé Gouet , Theophile Ndougsa Mbarga , Adoua Njueya kopa
Published: March 2022

Crystalline basement formations are originally without any hydrogeological potential  due to the poor primary porosity of rocks that they content. Therefore, finding productive zones of groundwater in those area becomes possible by a better knowledge of fracturing. The mapping of linear features on remotely sensed data is one of the keys to understand groundwater occurrence, in those area. For that purpose, The extraction of lineaments is a preliminary step in the selection of favorable sites for exploration of basement aquifers. The purpose of this work is to study the hydraulic role of the major lineaments of the Mayo-Banyo department (Adamawa Region, Cameroon) in the productivity of the boreholes through the analysis of the correlation between the productivity parameters of the high flow boreholes and the proximity to the major lineaments associated with other productivity index parameters such as topography, drilling depth, proximity to the nearest fracture node, lineament density and alteration thickness, and to suggest favorable areas for future hydraulic surveys. The application of the Canny filter and the shaded relief respectively on the Landsat 8 and SRTM image of the study area shows after statistical analysis a good correlation between the two methods in view of the obtained directional rosettes for lineaments. The main directions of the extracted lineaments are N-S, NNE-SSW and NNW-SSE. The NNE-SSW direction as indicated by several structural studies carried out in the region corresponds to the orientation of the D1 phase of tangential tectonics that the region underwent. The correlation analysis between the productivity of high flow boreholes (Q> 5 m3/h) and the proximity to major lineaments (L> 5km) of the NNE-SSW directional class shows a positive and relatively significant correlation (0.41). This result shows that the productivity of the boreholes in the department is influenced by the proximity to the main fracture directions. In addition, the analyses also show a combined effect of lineament density, topography (slope), borehole depth and alteration thickness on the productivity of high flow boreholes. The northeast and west of the study area have a greater density of lineaments. These areas would have been affected by greater structural deformation and therefore may have a higher potential for groundwater infiltration due to a greater density of lineaments that serve as weaving. Therefore, more local analysis with geophysical data in those areas should help to better locate future wells. 
Keywords: lineaments, basement aquifers, Mayo-Banyo.
Predicting high-frequency nutrient dynamics in the Danube River from surrogates with sensors and machine-learning
Authors: Huang Jingshui , Tran Binh Yen , Arias-Rodriguez Leonardo F.
Published: March 2022

Nutrient dynamics play an essential role in aquatic ecosystems. Despite advances in sensor technology, nutrient concentrations are difficult and expensive to monitor in-situ and in real-time. Emerging data-driven methods may provide surrogate measures for nutrient concentrations. In this work, 4-year high-frequency (15-min interval) regularly monitored variables and 2 data-driven algorithms are used to build surrogate measures for nitrate, orthophosphate, and ammonium at 2 stations in the German part of the Danube River. The variables used as input futures are dissolved oxygen (DO), temperature (Temp), conductivity (EC), pH, discharge rate (Q), and chlorophyll-a (Chl-a). Multiple linear regression (MLR) and Random Forest Regression (RF) are trained and cross-validated for the concentration predictions of nutrient constituents. Prior to training, pre-processing procedures were implemented, including removing outliers and filling missing values by linear interpolation. This work presented a thorough description of the workflow, including intermediate steps for feature engineering, feature selection, hyper-parameter optimization. The results of the 12 surrogate models (2 algorithms * 3 constituents * 2 stations) are compared. The results show that the RF algorithm can reproduce the environmental phenomena and contribute to water quality management. The RF algorithm already outperformed MLR when adding at least three predictors in this work. The five-fold CV has identified the reliable and stable prediction of the targets NO3--N (R2 = 0.9967 and 0.9992), NH4+-N (R2 = 0.9861 and 0.9927), PO43--P (R2 = 0.9638 and 0.9643).
Forecasting impacts of tropical cyclones with machine learning : A case study in the Philippines 
Authors: Teklesadik Aklilu , van den Homberg Marc
Published: March 2022

Due to its geographical location, the Philippines is highly exposed to Tropical Cyclones (TC). Every year at least one TC will make landfall and cause significant humanitarian impact and economic loss. To reduce the humanitarian impact of TC, the Philippine Red Cross with the German Red Cross and 510, an initiative of The Netherlands Red Cross, designed and implemented a Forecast Based Financing (FbF) system. The early actions in the FbF system are pre-identified and will be triggered when an impact-based forecasting model indicates a pre-defined danger level will be exceeded. This research develops and evaluates multiple ML algorithms for classification and regression with a lead time of 120 to 72hrs before TC landfall. The algorithms are trained on around 40 historical typhoon events and xx predictors on the hazard, vulnerability, coping capacity, and exposure are used. The classification model predicts if 10% of buildings in a municipality are completely damaged or not. The regression model gives the percentage of buildings that are completely damaged in a municipality. The RandomForest algorithm outperformed other algorithms for both classification and regression for both training and validation datasets. The ML models performed better than a baseline model (a wind-damage curve per building type) for the historical typhoon events. The Philippine Red Cross has been using the ML model since 2019, whereby actual forecast information from ECWMF replaces the historical hazard information at landfall. However, the ML impact-based forecasting model cannot be better than the hazard information that goes into it. Those typhoons that rapidly intensify cannot be captured at the cutoff of 72 hrs lead time (the minimum time required to start up early actions). But for the other typhoons, ML is very beneficial as a trigger tool for activating early actions and can support the reduction of the impact of typhoons on vulnerable communities.
Arctic Amplification through Inter-seasonal Feedback Effect in Past Interglacials 
Authors: Hirose Lynn , Abe-Ouchi Ayako , Yoshimori Masakazu , Chan Wing-Le , O'ishi Ryouta , Obase Takashi
Published: March 2022

Past interglacials allow investigating the climatic processes and associated feedbacks during warm periods, which are characterized by different combinations of climatic forcing such as solar radiation, GHGs, and ice sheets. Arctic warming amplification, a common phenomenon between past interglacials and present warming, has seasonality in its feedback mechanism, and detailed study of these internal feedbacks is still lacking despite its global impact. In this study, the simulation experiments under conditions close to the past interglacial periods (MIS1; Holocene, MIS5e; Last Interglacial, and MIS11) are conducted using a coupled atmosphere-ocean-vegetation model MIROC (4m) AOVGCM, particularly focusing on the role of ice sheets and Arctic sea ice. Climate responses to inputs and conditions are compared to examine the seasonal effects of atmosphere-ocean-ice feedbacks on Northern hemisphere high-latitudes temperature. Ice sheet distribution is set as a boundary condition in addition to the orbital elements, land cover, and GHGs to account the effect of remaining ice sheets at the timing of peak insolation. Feedback Analysis is also conducted to quantify the contribution of each feedback element to the surface temperature change. It is demonstrated that an inter-seasonal effect of air-sea-ice-vegetation feedbacks contributes to Arctic warming amplification, where heat gained in summer is used for sea ice melting and ocean absorption, and is released in autumn and winter, resulting in annual warming. This process is amplified when considering vegetation feedbacks and seen commonly in MIS1, 5e, and 11. In periods when ice sheets remain, Arctic sea ice keeps a high degree of concentration in summer, and annual mean temperatures at Northern high latitudes are lower than would be expected from insolation intensity. These results imply that the presence of Northern hemisphere ice sheet has a significant effect on Arctic climate response to insolation intensity by suppressing feedbacks that contribute to Arctic amplification through the reduced melt of summer sea ice.
LOFAR ionospheric scintillation spectral measurements in mid-latitude region
Authors: Pożoga Mariusz , Ciechowska Helena , Matyjasiak Barbara , Grzesiak Marcin , Rothkaehl Hanna , Wronowski Roman , Tomasik Łukasz , Beser Katarzyna
Published: March 2022

Due to its small intensity, ionospheric scintillation at the mid-latitudes is difficult to observe. The measurements of signal amplitude scintillation from GNSS in this region are almost impossible to perform with sufficient quality. 
The European interferometer LOFAR observing in the frequency range 10-90 MHz, provides a good opportunity to carry out complex studies on the ionospheric scintillation in the mid-latitudes. 
In this work, we show statistical analysis of amplitude scintillation intensity described by the S4 index as well as spectral parameters given from specially designed pipelines dedicated to computing and analyzing spectra obtained with a single LOFAR station and ILT observation. We also show temporal and spatial statistics for spectral index, Fresnel frequency, and noise level of measurement.
Revealing the liquefaction mechanism and anisotropy behaviour of root-reinforced soils: an energy-based approach
Authors: Karimzadeh Ali Akbar , Leung Anthony Kwan
Published: March 2022

Revealing the liquefaction mechanism and anisotropy behaviour of root-reinforced soils: an energy-based approach

 

Ali Akbar Karimzadeh, Anthony Kwan Leung

Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR

 

Abstract:

Recent physical modelling work has demonstrated that plant roots provide seismic resistance to geotechnical infrastructure such as slopes and pipelines against liquefaction. Indeed, there is evidence from published triaxial data that the presence of roots increased the liquefaction resistance of soil and changed the liquefaction failure mode from limited flow failure to cyclic mobility, depending on the amount of cyclic stress ratio applied and the available root volume. However, effects of root orientation on soil anisotropy and energy dissipation during the process of liquefaction, have not been adequately addressed in the literature. In this presentation, we will present a new energy-based framework and its application to reinterpret a set of published triaxial data concerning on the undrained strain-controlled cyclic behaviour of root-reinforced soils. Based on the framework, the changes in the amount of dissipated energy required to reach the liquefaction criteria (i.e. 5% double-amplitude axial strain) of the soil due to the presence of roots of different volume ratio will be determined. We will use this energy term and the strain values at the compressive and extensive sides of a cyclic loading at the liquefaction state to explore how root orientations would affect the soil anisotropy. A new correlation between normalised cumulative dissipated energy (∑ΔW/σc’, where σc’ is the effective confining pressure) and the cyclic resistance ratio at the cycle number of 15 (CRR15) will be established. We will also present the correlation between the ∑ΔW/σc’ with the normalised cumulative strain energy (∑4W/σc’) which is representative to the the demand energy of an earthquake event. Finally, we will discuss any effects of the recycling and recovering of strain energy upon cyclic loading, and their importance in the energy interpretation to root-reinforced soils.

Keywords: Energy-based approach, Root-reinforced soil, Anisotropy, Liquefaction, Triaxial cyclic tests
HelioSwarm: The Nature of Turbulence in Space Plasma
Authors: Klein Kristopher , Spence Harlan ,   The HelioSwarm Science Team
Published: March 2022

Quantifying the nature of turbulent fluctuations and the associated cascade of energy requires simultaneous measurements at multiple points spanning several characteristic length scales. Here, we present the HelioSwarm mission concept, which has been designed to reveal the three-dimensional, dynamic mechanisms controlling the physics of plasma turbulence. The HelioSwarm Observatory measures the plasma and magnetic fields with a novel configuration of spacecraft in the solar wind, magnetosheath, and magnetosphere. These simultaneous multi-point, multi-scale measurements span MHD, transition, and ion-scales, allowing us to address two overarching science goals: 1) Reveal the 3D spatial structure and dynamics of turbulence in a weakly collisional plasma and 2) Ascertain the mutual impact of turbulence near boundaries and large-scale structures. Addressing these goals is achieved using a first-ever "swarm" of nine spacecraft, consisting of a "hub" spacecraft and eight "node" spacecraft. The nine spacecraft co-orbit in a lunar resonant Earth orbit, with a 2-week period and an apogee/perigee of ~60/11 Earth radii. Flight dynamics design and on-board propulsion produce ideal inter-spacecraft separations ranging from fluid scales (1000's of km) to sub-ion kinetic scales (10's of km) in the necessary geometries to enable the application of a variety of established analysis techniques that distinguish between proposed models of turbulence. Each node possesses an identical instrument suite that consists of a Faraday cup, a fluxgate magnetometer, and a search coil magnetometer. The hub has the same instrument suite as the nodes, plus an ion electrostatic analyzer. With these measurements, the HelioSwarm Observatory promises an unprecedented view into the nature of space plasma turbulence.
Closing the Gap between Models and Observations: Deep Learning from Mismatches 
Authors: Patakchi Yousefi Kaveh , Kollet Stefan
Published: March 2022

Numerical weather prediction and climate models provide important information on essential atmospheric variables and extreme events. However, due to model uncertainties arising from initial value and model errors, the simulation results do not match in-situ or remotely sensed measured observations to an arbitrary accuracy. Machine Learning (ML) and/or Deep Learning (DL) methods have shown to be successful tools in closing the gap between models and observations due to high generalization skills and better representation of non-linear and complex relationships. This study focused on using UNet encoder-decoder Convolutional Neural Network (CNN) for extracting spatiotemporal features from model simulations and predicting the actual mismatches between the simulations results and a reference data set. Here, the model simulations serving as input to the CNN were obtained from climate simulations over Europe with the Terrestrial Systems Modeling Platform (TSMP-G2A). The reference data set representing observations was obtained from the COSMO-REA6 reanalysis. The proposed mismatch learning framework was applied to precipitation and surface pressure representing more and less chaotic variables, respectively. The study shows that UNet is able to learn the precipitation and surface pressure mismatches with a daily average correlation coefficient of 0.68 between the actual against predicted mismatches. Seasonal and regional intercomparisons of various precipitation types (e.g., stratiform rainfall, convective rainfall, and snowfall) reveal that the UNet faces challenges in learning the convective-type precipitation mismatches, which may be due to higher random uncertainties in model-based data. After training the UNet network, the reference data is no longer needed for generating the mismatch information. Thus, the UNet weights may be used online during the simulation or as a post-processor to correct predicted variables, which is useful in impact studies. In the first validation experiments, the corrected precipitation data show a strong improvement over the original simulated model data in mean error (47 % on average), correlation coefficient (37 % on average), and root mean square error (22 % on average).
Sustainable Reservoir Operation and Control Using a Deep Reinforcement Learning Policy Gradient Method
Authors: Sadeghi Tabas Sadegh , Samadi Vidya
Published: March 2022

The increasing stress on water resource systems has prompted researchers to look for ways to improve the performance of reservoir operations. Changes in demand, various hydrological inputs, and new environmental stresses are among issues that water managers face. These concerns have sparked interest in applying different techniques to determine reservoir operation policy to improve reservoir system performance. As the resolution of analysis rises, it becomes more difficult to effectively represent a real-world system using currently available approaches for determining the best reservoir operation policy. One of the challenges is the "curse of dimensionality," which occurs when the discretization of the state and action spaces becomes finer or when more state or action variables are taken into account. Because of the dimensionality curse, the number of state-action variables is limited, rendering dynamic programming (DP) and stochastic DP (SDP) ineffective in handling complex reservoir optimization issues. Reinforcement learning (RL) is one way to overcome the aforementioned curses of stochastic optimization of water resources systems. RL is a well-known and influential technique in machine learning research that can solve a wide range of optimization and simulation challenges. In this study, a novel continuous-action deep RL algorithm called Deep Deterministic Policy Gradients (DDPG) is applied to solve the DP problem for the Folsom Reservoir system located in California, US. Without requiring any model simplifications or surrendering any of the critical characteristics of DP, the employed continuous action-space RL method effectively overcomes dimensionality concerns. The system model employs an iterative learning method that takes into account delayed rewards without requiring an explicit probabilistic model of hydrologic processes, and it can learn the best actions that maximize total expected reward by interacting with a simulated environment. This research is funded by the US Geological Survey.