bluuebunny/crossref_metadata_2025 huggingface dataset:
What is the heat uptake potential of Antarctic Bottom Water?
Authors: Zika Jan , Savita Abhishek , Holmes Ryan , Sohail Taimoor
Published: March 2021

Antarctic Bottom Water (AABW) is a cold dense water mass which sinks around Antarctica keeping the abyssal ocean relatively cool. Recent observations have suggested a component of recent deep ocean warming is linked to AABW. Here we explore how much changes in AABW could affect changes in vertical ocean heat transport in a warming climate. If the AABW circulation were to be completely extinguished, for example due to increases in upper ocean thermal stratification, AABW would cease to cool the deep ocean and hence lead to an effective warming of the abyss. Therefore, we propose that long term mean vertical heat transport of the AABW circulation is an effective upper bound on the change in heat transport that can be affected by changes in AABW. We call this upper bound the ‘heat uptake potential’. We analyse AABW circulations in an ensemble of numerical climate models. We find that the AABW circulation contributes between 0.05Wm-2 and 0.15Wm-2 to the global vertical heat balance in the model’s pre-industrial states. Indeed, under abrupt CO2 forcing changes, AABW heat transport systematically reduces (in some cases completely), with the largest reductions occurring in models with the largest pre-industrial mean heat transports. The AABW circulation vertical heat transport is found to be highly correlated with the minimum of the Meridional Overturning Circulation at 50oS in the models, suggesting there may be observable constraints on the heat uptake potential of AABW.
Integration of evapotranspiration estimates from scaled sap flow values and eddy covariance measurements in the BRIDGET toolbox
Authors: Hassler Sibylle K. , Dietrich Peter , Kiese Ralf , Mälicke Mirko , Mauder Matthias , Meyer Jörg , Rebmann Corinna , Strobl Marcus , Zehe Erwin
Published: March 2021

Comparing estimates of evapotranspiration (ET) from different in-situ measurements – or between in-situ measurements and remote sensing products or modelling outputs – always entails the challenge of different scales and method-specific uncertainties. Especially when the estimates originate in different research disciplines, addressing and quantifying the various sources of uncertainty of the scaled ET values becomes a difficult task for individual researchers who are not familiar with all the methodological details.

The BRIDGET toolbox – developed within the Digital Earth project – wants to support the integration and scaling of diverse in-situ ET measurements by providing tools for storage, merging and visualisation of multi-scale and multi-sensor ET data. This requires an appropriate metadata description for the various measurements as well as an assessment of method-specific uncertainties which need to be supported by domain experts. We combine these tools in a standalone python package and also implement them in an existing virtual research environment (V-FOR-WaTer).

Our first use case defines and quantifies the various sources of uncertainty when scaling sap flow values from individual sensor measurements in a tree up to the transpiration estimate of a stand. Comparison estimates come from eddy covariance measurements, lysimeters and remote sensing products.
ENACTS: Transforming Climate Services Across Africa
Authors: Dinku Tufa
Published: March 2021

Despite recent and mostly global efforts to promote climate services in developing countries, Africa still faces significant limitations in its institutional infrastructure and capacity to develop, access, and use decision-relevant climate data and information products at multiple levels of governance. The Enhancing National Climate Services (ENACTS) initiative, led by Columbia University’s International Research Institute for Climate and Society, strives to overcome these challenges by targeting the way climate-sensitive decisions are made at the local, regional, and national levels. The ENACTS approach is executed by working directly with the National Meteorological and Hydrological Services (NMHS) to build capacity for improving the availability, access, and use of quality climate data and information products at relevant spatial and temporal scales. The ENACTS approach has shown to be an effective means to transform decision-making surrounding vulnerabilities and risks at both national and local scales in over a dozen countries at the national level as well as at regional level East and West Africa. In the ENACTS approach, challenges to the availability of climate data are alleviated by combining quality-controlled station observations with global proxies to generate spatially and temporally complete climate datasets. Access to climate information is enhanced by developing an online mapping service that provides a user-friendly interface for analyzing and visualizing climate information products. Use of the generated climate data and the derived information products are promoted through raising awareness in relevant communities, training users, and co-production processes.
Quantification of Water Sustainability in Upper Indus River Basin Through the Concept of Resilience, Reliability and Vulnerability (RRV)
Authors: Rahim Akif , Tariq Nadeem , Aziz Farhan , Yousaf Muhammad , Khurshid Tahira
Published: March 2021

The sustainability index identifies a strategy that defend or improve the desired water management features of the basin in the future. The Upper Indus river basin is a high mountain region and consider third freshwater tower. The flow of the river consists of melting glaciers, snow, rainfall. Beyond the polar regions, the Upper Indus Basin has the largest area of glaciers in the world (22,000 km2).  About 220 million people depend on Indus Basin water for agriculture and drinking purpose. Under the changing climate, sustainability is becoming a challenge for the freshwater resources. The integration of climate variables with RRV indicators is a new approach to meet this challenge. In this study the sustainability of the upper Indus is quantified. The probabilistic concept of resilience, reliability and vulnerability is applied to rainfall variability and drought patterns. The monthly Standardized Precipitation-Evapotranspiration Index (SPEI) grided data (0.5o 0.5o) generated by climate research unit (CRU)version 4 has been used for study during the period 1901–2018. Based on the SPEI pattern, the SPEI of -0.5 was selected as the threshold (demand) to evaluate the sustainability. The results indicate the frequency of drought events in the western part of the basin is much higher than the eastern part. However, the frequency of drought events in the basin is high but the capability of the basin to resilient the droughts varies from 0.57 to 0.83. The value of reliability indicator varies from 0.8 to 0.86 and vulnerability of drought in the basin is in the range of 0.2 to 0.45. The average water sustainability index of the basin is 0.4 which lies in the category of a satisfactory state.The results of the conceptual framework of RRV can provide a more comprehensive basis for designing watershed health variables and drought management plans.

 

Keywords: Upper Indus Basin, Water sustainability, RRV concept, SPEI, Drought.
Heterogeneous sulphate formation in the aerosol, the cloud and the frost
Authors: Chen Hui , Li Jiarong , Zhu Chao , Herrmann Hartmut , Chen Jianmin
Published: March 2021

The rapid formation of sulphate is the main driving force behind the explosive growth of PM2.5 in China. Our comprehensive study, combined with field observations, laboratory simulations and modelling, indicated that high concentration of hydroperoxide (H2O2) from heterogeneous reactions significantly promoted sulphate formation in winter north China. Unexpectedly, during the same campaign, a high proportion of sulphate has been observed in the frost. The chemical composition of the frost appeared to be independent of that of PM2.5. These findings can be important for the removal rate of SO2 in the atmosphere and for the occult deposition of sulphate.

Also, we have investigated the contribution of oxidation channels to sulphate formation in the cloud at the summit of Mt. Tai (1545 m) in summer. Our results suggested that dissolved ozone is the dominant oxidant for the oxidation of S(IV), especially when the pH of the cloud water is less acidic (> 5.5). In recent years, with the increase of ozone concentration in China, the sulphate formation by ozone in the cloud will continue to be pronounced.

 

References:

Zhu, C., Li, J.R., Chen, H., Cheng, T.T., Wen, L., Herrmann, H., Xiao, H., Chen, J.M., 2020. Inorganic composition and occult deposition of frost collected under severe polluted area in winter in the North China Plain. Science of the Total Environment 722.

Li, J.R., Zhu, C., Chen, H., Zhao, D.F., Xue, L.K., Wang, X.F., Li, H.Y., Liu, P.F., Liu, J.F., Zhang, C.L., Mu, Y.J., Zhang, W.J., Zhang, L.M., Herrmann, H., Li, K., Liu, M., Chen, J.M., 2020. The evolution of cloud and aerosol microphysics at the summit of Mt. Tai, China. Atmospheric Chemistry and Physics 20, 13735-13751.

 
How föhn events of different type characterize the local climate of Southern Patagonia
Authors: Temme Franziska , Turton Jenny , Mölg Thomas , Sauter Tobias
Published: March 2021

The local climate of Southern Patagonia is strongly influenced by the interaction between the topography and persistent westerlies, which can generate föhn events. These events are characterized by warm, dry and windy conditions in the lee together with an increase in solar radiation, all factors that can strengthen local glacier ablation. The upstream flow regime influences the generation of different types of föhn which dictate the lee-side atmospheric response regarding the strength, spatial extent and phenomenology. On the basis of the phenomenological character of föhn types, inferences may be drawn on the spatial variability of the local climate and thus also on the glacier impact.

We use a combination of observational data from four automatic weather stations (AWSs) and high-resolution numerical modeling with the Weather Research and Forecasting (WRF) model for a region in Southern Patagonia (48° S–52° S, 72° W–76.5° W) including the Southern Patagonian Icefield (SPI). In order to resolve the complex topography of the region, the model was set up using one-way grid nesting over three domains, which increases the resolution from 20 km in the parent domain to 4 km and 1 km in the nested domains. The final model parametrizations were selected based on 20 sensitivity runs. Evaluation of WRF model surface variables against the AWS data showed that overall the atmospheric fields and the föhn signals were well reproduced, however we found an overestimation in wind velocities.

The application of a föhn identification algorithm to a 10-month study period (June 2018–March 2019) reveals 81 föhn events in total. A simulation of three events of differing flow regimes (supercritical, subcritical, transition) suggests that a supercritical flow regime leads to a linear föhn event with a large spatial extent but moderate intensity. In contrast, a spatially limited but locally strong föhn response is induced by a subcritical regime with upstream blocking and by a transition regime with a hydraulic jump present. Our results imply that the hydraulic jump-type föhn event (transition case) is the most critical for glacier mass balances since it shows the strongest warming, drying, wind velocities and largest solar radiation increases over the SPI. The consideration of flow regimes over the last 40 years shows that subcritical flow occurs most frequently (78%), however transitional flow occurs 14% of the time, implying the potential impact on Patagonian glaciers.
A novel toolkit to streamline Land Use Land Cover change assessment in the SWAT+ model to enhance flood management and infrastructure decisions
Authors: Rigby Alex , Patil Sopan , Ritsos Panagiotis
Published: March 2021

Land Use Land Cover (LULC) change is widely recognised as one of the most important factors impacting river basin hydrology.  It is therefore imperative that the hydrological impacts of various LULC changes are considered for effective flood management strategies and future infrastructure decisions within a catchment.  The Soil and Water assessment Tool (SWAT) has been used extensively to assess the hydrological impacts of LULC change.  Areas with assumed homogeneous hydrologic properties, based on their LULC, soil type and slope, make up the basic computational units of SWAT known as the Hydrologic Response Units (HRUs).  LULC changes in a catchment are typically modelled by SWAT through alterations to the input files that define the properties of these HRUs.  However, to our knowledge at least, the process of making such changes to the SWAT input files is often cumbersome and non-intuitive.  This affects the useability of SWAT as a decision support tool amongst a wider pool of applied users (e.g., engineering teams in environmental regulatory agencies and local authorities).  In this study, we seek to address this issue by developing a user-friendly toolkit that will: (1) allow the end user to specify, through a Graphical User Interface (GUI), various types of LULC changes at multiple locations within their study catchment, (2) run the SWAT+ model (the latest version of SWAT) with the specified LULC changes, and (3) enable interactive visualisation of the different SWAT+ output variables to quantify the hydrological impacts of these scenarios.  Importantly, our toolkit does not require the end user to have any operational knowledge of the SWAT+ model to use it as a decision support tool.  Our toolkit will be trialled at 15 catchments in Gwynedd county, Wales, which has experienced multiple occurrences of high flood events, and consequent economic damage, in the recent past.  We anticipate this toolkit to be a valuable addition to the decision-making processes of Gwynedd County Council for the planning and development of future flood alleviation schemes as well as other infrastructure projects.
Curie point depth and heat flow maps deduced from magnetic data of Gonghe Basin, China
Authors: Wang Zhuo , Zeng Zhaofa
Published: March 2021

Most recently, energy consumption around the world steps into a new situation divided by petroleum, natural gas, coal and new energy. Fossil fuels are disputed for pollution and CO2 emission, and geothermal energy is popular as a clean, ecofriendly and renewable new energy, which can be used for power generation or direct application (e.g. bathing, building heating).

Gonghe Basin, located in the western part of China, has been thought as a potential geothermal field since 1989. To investigate geothermal distribution in Gonghe Basin and adjacent area, magnetic data is used in this paper. Firstly, we proposed an improved magnetic interface inversion method based on traditional Park-Oldenburg method. This improved method introduces dual geological interfaces instead of one interface, variable magnetic susceptibility instead of constant magnetic susceptibility and upward continuation in a form equivalent to inversion iteration in the Fourier domain instead of the divergent, downward continuation term, to improve suitability and precision of the inversion method. Then Curie point depth (CPD) map and heat flow map could be deduced from magnetic data through the improved Park-Oldenburg method.

The CPDs range from 16 to 25.5 km and heat flow values range from 61 to 91 mW/m2. What's more, we take faults and seismic activities into account, we find that study area has greater geothermal potential in eastern part with shallower CPD, higher heat flow values and more active subsurface structure. Considering with known geothermal value in actual measurement, the results indicate high heat flow value in Gonghe Basin is coaction of high thermal background, radiogenic heat and partial geothermal anomalous heat source. 
A new Method for Fault-Scarp Detection Using Linear Discriminant Analysis (LDA) in High-Resolution Bathymetry Data From the Alarcón Rise and Pescadero Basin, Gulf of California.
Authors: Vega Ramirez Luis Angel , Splez Madero Ronald Michael , Contreras Perez Juan , Caress David , Clague David A. , Paduan Jennifer B.
Published: March 2021

The mapping of faults and fractures is a problem of high relevance in Earth Sciences. However, their identification in digital elevation models is a time-consuming task given the resulting networks' fractal nature. The effort is especially challenging in submarine environments, given their inaccessibility and difficulty in collecting direct observations. Here, we propose a semi-automated method for detecting faults in high-resolution gridded bathymetry data (~1 m horizontal and ~0.2 m vertical) of the Pescadero Basin in the southern Gulf of California, which were collected by MBARI's D. Allan B autonomous underwater vehicle. This problem is well suited to be explored by machine learning and deep-learning methods. The method learns from a model trained to recognize fault-line scarps based on key morphological attributes in the neighboring Alarcón Rise. We use the product of the mass diffusion coefficient with time, scarp height, and root-mean-square error as training attributes. The method consists of projecting the attributes from a three-dimensional space to a one-dimensional space in which normal probability density functions are generated to classify faults. The LDA implementation results in various cross-sectional profiles along the Pescadero Basin show that the proposed method can detect fault-line scarps of different sizes and degradation stages. Moreover, the method is robust to moderate amounts of noise (i.e., random topography and data collection artifacts) and correctly handles different fault dip angles. Experiments show that both isolated and linkage fault configurations are detected and tracked reliably.
UAV-based cm-scale mapping of biofilms and Chl-a patterns in glacial forefields using visible band ratios
Authors: Roncoroni Matteo , Mancini Davide , Kohler Tyler Joe , Miesen Floreana Marie , Gianini Mattia , Battin Tom Ian , Lane Stuart Nicholas
Published: March 2021

Biofilms have received great attention in the last few decades including their potential contribution to carbon fluxes and ecosystem engineering in aquatic ecosystems. Quantifying the spatial distribution of biofilms and their dynamics through time is a critical challenge. Satellite imagery is one solution, and can provide multi- and hyper-spectral data but not necessarily the spatial resolution that such studies need. Multi- and hyper-spectral data sets may be of particular value for not simply detecting the presense/absence of biofilms but also indicators of primary productivity such as chlorophyll-a concentrations. Spatial resolution is sensor quality dependent, but also controlled by sensor elevation above the ground. Hence, higher resolutions can be achieved either by using a very expensive sensor or by decreasing the distance between the target area and the sensor itself. To date, sensor technology has advanced to a point where multi- or even hyper-spectral cameras can be easily transported by UAVs, potentially yielding wide-range spectral information at unprecedented spatial resolutions. That said, such set ups have often exorbitant costs (several 1000s of US$) that few research institutions can afford or, due to the high probability of sensor lost, are risky to use. This is particularly true for glacier forefields where low air temperatures, dust and sudden wind gusts can easily damage both UAV and sensor components.

In this paper we test the performance of visible band ratios for mapping both biofilms and chlorophyll-a concentrations in an alpine glacier forefield characterized by a well-developed and heterogeneous (kryal, krenal and rhithral) stream system. The paper shows that low-cost and consumer grade UAVs can be easily deployed in such extreme environments, delivering high temporal resolution datasets and with sufficient quality RGB images for photogrammetric (SfM-MVS) processing and post-processing image analysis (i.e., band ratios). This paper shows also that visible band ratios correlates with chlorophyll-a concentrations yielding reliable chlorophyll-a information of the forefield and at the centimetric scale. This in turn allows for precise identification of the environmental conditions that lead to both biofilm development and removal through perturbation.
Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning
Authors: Kim Minsu , Kuhlmann Gerrit , Emmenegger Lukas , Brunner Dominik
Published: March 2021

Nitrogen oxides (NOx  = NO+ NO2) are harmful to human health and are precursors of other key air pollutants like ozone (O3) and particulate matter (PM). Since the lifetime of NOx is short and its main sources are anthropogenic emissions like fuel combustion from traffic and industry, near-surface NOx concentrations are highly variable in space and time. To assess the impact of NO2 on public health, maps of high spatial and temporal resolution are critical. In this study, we present hourly near-surface NO2 concentrations at 100 m resolution for Switzerland and northern Italy that are produced using machine learning, specifically an extreme gradient-boosted tree ensemble. The model was trained with in situ observations from European Air Quality e-Reporting data repositories (Airbase). Satellite NO2 observations from the TROPospheric Monitoring Instrument (TROPOMI) were compiled together with land use data, meteorological data and topography as covariates. Evaluation against in situ observations not used for the training shows that the dynamic maps produced in this study reproduce the spatio-temporal variation in near-surface NO2 concentrations with high accuracy (R2 = 0.59, MAE = 7.69 µg/m3). In addition, we demonstrate how public health studies can utilize such high-resolution maps for unbiased assessment of population exposure that can account for home addresses and mobility of individuals. Comparing the relative importance of the different covariates based on two different metrics, total information gain and averaged local feature importance, show a leading contribution of the TROPOMI observations despite their rather coarse resolution (3.5 km × 5.5 km) and daily update. TROPOMI NO2 observations were particularly important for the quality of the NO2 maps during periods of unusual NO2 reductions (e.g., during COVID19 lockdown) and when detailed emission-related covariates like traffic density, that may not be available in other regions of the globe, were not included in the model. Since all data used in our study are publicly available, our approach can be readily extended to other regions in Europe or applied worldwide.
A sequential calibration technique to improve IRI using TEC estimates of the GNSS network in Europe
Authors: Forootan Ehsan , Kosary Mona , Farzaneh Saeed , Schumacher Maike
Published: March 2021

The development of space-geodetic observation techniques has brought out a wide range of applications such as positioning and navigation, where the Global Navigation Satellite System (GNSS) is the main tools to provide surveying measurements in these applications. Though GNSS signals enable the calculation of receiver's position, some errors restrict their accuracy. Among these errors, the ionospheric delay is considered as an important error source in the Standard Point Positioning (SPP) applications. Empirical ionospheric models such as Klobuchar, International Reference Ionosphere (IRI), and NeQuick are often applied for computing the Total Electron Content (TEC) within ionosphere and its equivalent delays. However the simulation and forecasting skills of these models are limited due to the simplified model structures and model sensitivity to the calibration period. In this study, we present a novel sequential Calibration approach based on the Ensemble Kalman Filter (C-EnKF) to improve the performance of TEC estimations for SPP applications. To demonstrate the results, the IRI model is used as our basis and the TEC estimates from 56 IGS stations in Europe are applied as observation. The C-EnKF is applied to calibrate some selected model parameter so that IRI can be tuned over Europe. The numerical assessments are performed against the TEC estimates from dual frequency GNSS measurements and against the final IONEX products (that are available with 11 days delays). Based on the forecasting results (during September 2017), we show that the accuracy of TEC estimates from the C-EnKF is improved in the range of 3.7-64.87% compared to IRI. Keywords: Ionosphere, Sequential Calibration, Ensemble Kalman Filter (EnKF), IRI, Total Electron Content (TEC), Standard Point Positioning (SPP), GNSS
The Structure of Millennial Scale Glacial Climate Variability
Authors: Vettoretti Guido , Ditlevsen Peter , Jochum Markus , Rasmussen Sune
Published: March 2021

The Dansgaard-Oeschger (D-O) oscillation recorded in isotopic analyses of Greenland ice cores is a climate oscillation with millennial scale variability alternating between cold stadial climate and warm interstadial climate states. Using a series of long comprehensive climate model integrations of the glacial climate system under different levels of radiative forcing, we formulate a simple heuristic model to emulate the D-O oscillation. We demonstrate that the D-O oscillation has properties that are consistent with an internal unforced oscillation as well as displaying interesting behaviour that is consistent with noise induced transitions. Therefore, the D-O oscillation is more aptly characterized as a stochastic oscillator with stadial and interstadial durations that are more dependent upon a control parameter and internal climate variability rather than an intrinsic characteristic timescale.
Towards modelling magmatic systems using a staggered grid finite difference method
Authors: Berlie Nicolas , Kaus Boris , Popov Anton , Arts Mara , Riel Nicolas , Kiss Daniel
Published: March 2021

The dynamics of magmatic systems remain poorly understood, due to the lack of resolving power of geophysical methods to study active systems and the difficulty of interpreting exposed crystallized magma bodies. Numerical models are therefore helpful to connect the dots between classical geological studies, using rheological information and geometries derived from field or geophysical investigations to shed new lights on the mechanisms involved in such systems.

Taking advantage of the big CPU clusters currently available and the development of the DMStag framework as part of the PETSc infrastructure, the ERC-funded MAGMA project aims to build tools to analyse magmatic processes in the lithosphere. We developed a finite-difference staggered grid code solving the Stokes equations for visco-elasto-plastic rheologies and using analytical jacobians for linear and non-linear solvers, combined with regularized plasticity. The code is combined with both a marker and cell and semi-lagrangian advection schemes, is fully parallel and includes automated testing.

Here, we provide application examples ranging from simple benchmark validations against analytical solutions to more complex settings taking advantage of the broad rheologies and local heterogeneities permitted by high resolution settings and the finite difference method. Ongoing technical developments include adding two-phase flow and coupling to it with thermodynamic calculations to track the evolving chemistry of magmatic systems.
A new 2-moment microphysical scheme for studying hail initiation and growth: schemes comparison
Authors: Taufour Marie , Wang Chien
Published: March 2021

Southwestern France is an important wine region where hail-producing storms could cause considerable economic loss. To study the initiation and growth of hailstone, a new microphysical scheme based on the LIMA (Liquid, Ice, Multiple Aerosols, Vié et al., 2016) has been developed. The original LIMA only contains two-moment scheme for rain water, cloud water, and ice crystal. Whereas, the other ice hydrometeors are described by a single-moment scheme. The new scheme adds a full two-moment framework to snow, graupel, and hailstone, thus allowing a better representation of the microphysical processes than the original partial two-moment approach could offer. An idealized severe storm case has been simulated and have been used to evaluate the performance of the single-moment ICE3 scheme, the partial two-moment LIMA scheme, and the new full two-moment scheme in reproducing the evolution of observed hail-producing storm cases. The difference as well as similarity in modeled structures of the storms including hailstone development by different microphysics schemes and using different aerosol loadings are examined and will be presented.
Design of rain gauge network using radar and road network
Authors: Kwon Taeyong , Yoon Sanghoo
Published: March 2021

Uncertainty in the gauged network can lead to inaccuracies in dam operations. Entropy is a well-known measurement of uncertainty. Goesan Dam has a small basin area and is affected by a small amount of precipitation, and Hwacheon Dam is contained outside the territory of South Korea, making it difficult to observe the water flow. The observed gauged precipitation and radar data on rainy days were considered between 2018 and 2019. Choosing appropriate radar were performed based on the priority of the rainfall gauge network using conditional entropy. This is because the rainfall gauge network is the actual precipitation and it can only cover certain points. However, the radar is the cloud reflectivity of a large area. Therefore the location of additional rain spots was selected through conditional entropy of highly consistent radar data. Nevertheless, there might be difficulties in installing gauged equipment in reality. So the optimal rainfall network was designed in consideration of the road network. As a result, the uncertainty of precipitation in Goesan Dam and Hwachoen Dam could be decreased by 63.3% and 67.9% respectively when three additional potential rain points were operated without any restriction. The uncertainty in the Goesan Dam basin and Hwachoen Dam would be reduced up to 55.3% and 65.0% when three additional potential rain points were installed nearby the road network. Therefore, through the proposed method, an optimal rainfall network can be designed by balancing cost and uncertainty.

This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD (No. 2018-Tech-20)
In-situ measurements of stress anisotropy in natural debris flows 
Authors: Nagl Georg , Hübl Johannes , Kaitna Roland
Published: March 2021

Stress anisotropy affects the motion of gravitational mass flows, including debris flows, rock and snow avalanches. Though widely used in analytical models and numerical simulation tools, direct measurements of stress anisotropy in debris flows are not yet available. The present study aims to investigate the ratio of longitudinal to normal pressure exerted by two natural debris flows impacting a monitoring structure in the Gadria creek, IT. The fin-shaped structure in the middle of the channel is equipped with a force plate upstream of the barrier and load cells on the vertical wall of the barrier, continuously recording forces in flow and bed-normal direction. Additionally, the flow height and basal pore fluid pressure were measured. Here we present data from surges of two debris-flow events with peak flow heights of 2.5 m and velocities up to 4 m/s. The ratio of pore fluid pressure to normal stress (often termed liquefaction ratio) reached values up to 0.8. We find an anisotropic stress state during most of the flow event, with stress ratios ranging between 0.1 and 3.5. Video recordings reveal complex deposition and re-mobilization patterns in front of the barrier during surges and highlight the unsteady nature of debris flows. We find a correlation of the stress ratio with flow depth. There is a weak correlation between stress ratio and liquefaction ratio during the falling limb of the surge hydrographs.  Our monitoring data confirm the assumption of stress anisotropy in natural debris flows and support the earth-pressure concept used for gravitational mass flows.
Data Fusion of Total Solar Irradiance Composite Time Series Using 40 years of Satellite Measurements: First Results 
Authors: Montillet Jean-Philippe , Finsterle Wolfgang , Schmutz Werner , Haberreiter Margit , Sikonja Rok
Published: March 2021

Since the late 70’s, successive satellite missions have been monitoring the sun’s activity, recording total solar irradiance observations. These measurements are important to estimate the Earth’s energy imbalance, i.e. the difference of energy absorbed and emitted by our planet. Climate modelers need the solar forcing time series in their models in order to study the influence of the Sun on the Earth’s climate. With this amount of TSI data, solar irradiance reconstruction models  can be better validated which can also improve studies looking at past climate reconstructions (e.g., Maunder minimum). Various algorithms have been proposed in the last decade to merge the various TSI measurements over the 40 years of recording period. We have developed a new statistical algorithm based on data fusion.  The stochastic noise processes of the measurements are modeled via a dual kernel including white and coloured noise.  We show our first results and compare it with previous releases (PMOD,ACRIM, ... ). 
Effects of the aquifer heterogeneity in heat production from low enthalpy geothermal systems
Authors: Zarlenga Antonio , Di Dato Maria Ines , D'Angelo Claudia , Casasso Alessandro
Published: March 2021

Open-loop shallow geothermal systems, which exploit shallow aquifers as a heat source or sink, have a great potential to reduce greenhouse gas emissions related to the heating and cooling of buildings. In order to limit the depletion of groundwater resources water is generally reinjected into the same aquifer after the heat exchange, as a consequence a thermal plume develops within the aquifer. Furthermore a share of the reinjected water may come back to the abstraction wells, inducing a progressive thermal alteration of the abstracted water temperature that may even result in the plant failure. This phenomenon, known as thermal recycling, strongly depends on the hydraulic conductivity of the aquifer. The design models commonly adopted in the practice assume a homogeneous domain with constant hydraulic conductivity, this assumption, however, is not realistic: neglecting the natural heterogeneity of hydraulic properties of the porous medium may result in large prediction errors.

In this study, we aim to quantify the impact of the different heat transport dynamics in aquifers on the thermal plume development. A stochastic model, which explicitly considers the spatial variability of the hydrological properties, such as the hydraulic conductivity, is developed for low enthalpy geothermal systems. The thermal breakthrough curve at the extraction well is obtained by applying a Lagrangian model and assuming a steady state velocity field. Relevant quantities of thermal recycling, such as the thermal breakthrough time, are adopted for the evaluation of the effects of the hydrogeological and geometrical parameters of the systems.

The results of our study emphasize how the correct representation of the aquifer heterogeneity is fundamental in the design of shallow geothermal systems and in the correct heat plume assessment.
Macroplastic Debris Transfer in Rivers: A Travel Distance Approach 
Authors: Newbould Robert , Powell Mark , Whelan Mick
Published: March 2021

Plastic accumulation in the marine environment is a major concern given the harmful effects and longevity of plastics at sea. Although rivers significantly contribute to flux of plastic to marine systems, plastic transport in rivers remains poorly understood and estimates of riverine plastic flux derived from field measurements and modelling efforts are highly uncertain. In this study, a new probabilistic model of plastic transport in rivers is presented which describes the main processes controlling displacement to predict the statistical distribution of travel distances for individual items of buoyant macroplastic debris. Macroplastic transport is controlled by retention in temporary stores (or traps) created by vegetation, bank roughness elements and other obstacles. The behaviour of these traps is represented in the model via a series of Bernoulli trials conducted in a Monte Carlo simulation framework. The probability of retention or release from traps is described using physical characteristics such as the type of vegetation, channel width or channel sinuosity index. The model was calibrated using a tracer experiment with six replicates, conducted in a small 1.1 km river reach. For each replicate, 90 closed air-filled plastic bottles were injected at the upstream end of the reach and the location of each bottle was recorded several times over a 24-hour period. Bottles were chosen as ‘model’ macroplastic litter items given their high usage and littering volume. Travel distances were low (the average distance travelled over 24 hours was 231 m and no bottles travelled more than 1.1 km, the length of the study reach) and variable (the coefficient of variation for the replicates ranged between 0.54 and 1.41). The travel distance distributions were controlled by the location and characteristics of discrete traps. The numerical model described the observed travel distance distributions reasonably well (particularly the trapping effect of overhanging trees and flow separation at meander bends), which suggests that modelling plastic transport for longer reaches and even whole catchments using a stochastic travel distance approach is feasible. The approach has the potential to improve estimates of total river plastic flux to the oceans, although significant knowledge gaps remain (e.g. the rate and location of plastic supply to river systems, the transport behaviours of different types of plastic debris in rivers and the effectiveness of different traps in different types of river system).
Upstream and downstream morphodynamic influence in simulated and real meandering rivers
Authors: amini Hossein , Zolezzi Guido , Monegaglia Federico , Olivetti Emanuele , Tubino Marco
Published: March 2021

This study investigates the dependency of meander lateral migration rates on the spatial distribution of channel centerline curvature in both synthetic and real meandering rivers. It employs Machine Learning techniques (hereafter ML) to relate observed local lateral meander migration rates with the local and the upstream/downstream values of the centerline curvature. To achieve this goal, it was primarily essential to identify the feasibility of using ML in the meandering river's morphodynamics. We then determined the ability of ML to predict the excess near bank velocity based a set of input data using different regression techniques (linear and polynomial, Stochastic Gradient Descent, Multi-Layer Perceptron, and Support Vector Machine). We then moved forward to study the upstream-downstream influence on local migration rate. Synthetic meandering river planforms, as obtained through the planform evolution model of Bogoni et al. (2017), which is based on Zolezzi and Seminara (2001) meander flow model, were used as test cases for the calibration and check of the different adopted ML algorithms. The calibrated algorithms were then applied to multi-temporal information on meander planform dynamics obtained through the PyRiS software (Monegaglia et al., 2018), to quantify to which extent the upstream and downstream distribution of meander centerline curvature affects the local meander migration rate in real rivers.

References 

1- Zolezzi, G., & Seminara, G. (2001b). Downstream and upstream influence in river meandering. Part 1. General theory and application overdeepening. Journal of Fluid Mechanics, 438(September 2015), 183–211. https://doi.org/10.1017/S002211200100427X

2- Monegaglia, F., Zolezzi, G., Güneralp, I., Henshaw, A. J., & Tubino, M. (2018). Automated extraction of meandering river morphodynamics from multitemporal remotely sensed data. In Environmental Modelling & Software (Vol. 105, pp. 171–186). https://doi.org/10.1016/j.envsoft.2018.03.028

3- Bogoni, M., Putti, M., & Lanzoni, S. (2017). Modeling meander morphodynamics over self-formed heterogeneous floodplains. In Water Resources Research (Vol. 53, Issue 6, pp. 5137–5157). https://doi.org/10.1002/2017wr020726

4- Benozzo, D.,  Olivetti, E., Avesani, P. (2017). Supervised Estimation of Granger-Based Causality between Time series. In Frontiers in Neuroinformatics. 

https://doi.org/10.3389/fninf.2017.00068 

5- Sharma A., Kiciman, E. (2020). DoWhy: An End-to-End library for Causal Inference. arXiv preprint arXiv:2011.04216. 

https://arxiv.org/abs/2011.04216
Machine learning based estimation of aboveground biomass in subalpine forests using Landsat 8 OLI and Sentinel-2B images in Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau
Authors: Luo Ke , Tang Xiaolu , Liu Liang , Luo Xinrui , Li Jingji
Published: March 2021

Although forests cover about one third of global land surface, forests act as important biophysical, biogeochemical, hydrological, economic and cultural roles in the Earth systems. Forests contribute up to 75% of terrestrial gross primary production and store more carbon in forest biomass and soil compared to the atmosphere. Forest aboveground biomass (AGB) plays a crucial role in regional and global ecological balance. However, due to the difficulties in measuring forest biomass in the field at regional scales, a quantitative estimation with high accuracy of forest AGB by linking remote sensing is still a challenge, particularly in mountainous region. Thus, we combined the Landsat 8 OLI and Sentinel-2B data to estimate subalpine forest AGB using linear regression (LR), and two machine learning approaches - random forest (RF) and extreme gradient boosting (XGBoost), with the linkage of field observations in Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau. A 10-fold cross validation (CV) method was used to evaluate the model accuracy, and then the proximity between the predicted value and the actual value was compared. The model efficiency (pseudo R2) and root mean square error (RMSE) were used as the accuracy evaluation criteria. Based on 54 field observations, results showed that mean forest AGB was 180.6 Mg ha-1with a strong spatial variability from 61.7 to 475.1 Mg ha-1. AGB varied significantly among forest types that AGB in coniferous forests was significantly higher than coniferous mixed forests and broad-leaved forests. Landsat 8 OLI and Sentinel-2B imagery were successfully applied to estimate AGB separately or combined. Integrating the Landsat 8 OLI and Sentinel-2B imagery significantly improved model efficiency for different modelling approaches. For the regression algorithms, machine learning method outperformed the linear regression. Among LR, RF and XGBoost approaches, XGBoost performed best with a model efficiency (R2) of 0.71 and root mean square error values of 46 Mg ha-1 and subsequently used for spatial modelling. Modelled results indicated a strong spatial variability in AGB, with a total 6.6×106 Mg across the study area. AGB distribution in the study area had obvious spatial characteristics, which was closely related to the elevation. It was mainly concentrated in the north and central areas, while in the southern region the AGB was relatively low, which was contrary to the trend of the elevation variation in the study area where the terrain was high in the south and low in the north. Our study highlighted a potential way to improve the estimate accuracy of forest AGB in mountainous region by integrating the Landsat 8 OLI and Sentinel-2B data using machine learning algorithms.
Impact of the measured parameters of exoplanets on the inferred internal structure.
Authors: Fernandez Otegi Jon , Dorn Caroline , Helled Ravit , Bouchy François , Haldemann Jonas , Alibert Yann
Published: March 2021

Exoplanet characterization is one of the main foci of current exoplanetary science. For super-Earths and sub-Neptunes, we mostly rely on mass and radius measurements, which allow us to derive the mean density of the body and give a rough estimate of the bulk composition of the planet. However, the determination of planetary interiors is a very challenging task. In addition to the uncertainty in the observed fundamental parameters, theoretical models are limited owing to the degeneracy in determining the planetary composition.
 We aim to study several aspects that affect the internal characterization of super-Earths and sub-Neptunes: observational uncertainties, location on the M-R diagram, impact of additional constraints such as bulk abundances or irradiation, and model assumptions.
 We used a full probabilistic Bayesian inference analysis that accounts for observational and model uncertainties. We employed a nested sampling scheme to efficiently produce the posterior probability distributions for all the planetary structural parameter of interest. We included a structural model based on self-consistent thermodynamics of core, mantle, high-pressure ice, liquid water, and H-He envelope. 
 Regarding the effect of mass and radius uncertainties on the determination of the internal structure, we find three different regimes: below the Earth-like composition line and above the pure-water composition line smaller observational uncertainties lead to better determination of the core and atmosphere mass, respectively; and between these regimes internal structure characterization only weakly depends on the observational uncertainties. We also find that using the stellar Fe/Si and Mg/Si abundances as a proxy for the bulk planetary abundances does not always provide additional constraints on the internal structure. Finally we show that small variations in the temperature or entropy profiles lead to radius variations that are comparable to the observational uncertainty. This suggests that uncertainties linked to model assumptions can eventually become more relevant to determine the internal structure than observational uncertainties.
Spatial downscaling method of glacier surface albedo based on deep learning
Authors: Xie Fuming , Liu Shiyin , Zhu Yu , Gao Yongpeng , Wu Kunpeng , Qi Miaomiao
Published: March 2021

Heat exchange in glacier region is strongly affected by the interaction between solar radiation and glacial surface, and albedo is an important index to quantitatively describe energy balance in this interaction process. Under the background of global warming, the observation and modeling of albedo are of great significance in the aspects including identification of snow and ice darkening or pollution, reconstruction of glacier mass balance and inversion of supraglacial debris expansion. However, insufficient observations, coupled with low spatial resolution of satellite derived products (250-1000m), make it difficult to analyze spatial changes at the glacier scale. A convolution neural network (CNN) contains one or more of the convolution layer, in which inputs are neighborhoods of pixels, resulting in a network that is not fully-connected, has great potential to the image segmentation but is also suited to identifying spatial patterns. Therefore, in this study, a CNN model—U-NET was trained to improve the spatial resolution of albedo products. In the U-NET, we took the shortwave black-sky albedo derived from moderate resolution imaging spectroradiometer (MODIS) boarded on Terra/Aqua satellite with a spatial resolution of 500m as response variable, and raw spectral information, band ratios, and color-to-grayscale conversion from Landsat 8 optical satellite imagery and the topographical components derived from SRTM DEM products as feature variables. The predicted albedo has been validated using observations form radiometer mounted on an automatic weather station at Yazgil glacier in Hunza valley, Karakoram. The results show that the accuracy of U-NET predicted albedo (RMSE = 0.071) is similar to that of MODIS albedo (RMSE = 0.074), which proved that U-NET has great application potential. The high spatial resolution albedo estimated by the model enhances its use in the analysis of spatial changes at the glacier scale, especially for small glaciers, but the optimization of its temporal resolution needs to be further studied.