bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of the mechanism of production of spray due to falling drops on the water surface in the framework of laboratory modeling of wind-wave interaction
Authors: Kandaurov Alexander , Ermakova Olga , Troitskaya Yuliya , Sergeev Daniil
Published: March 2022

A series of experiments was carried out on the Thermostratified Wind Wave Tank of IAP RAS, to study the processes of secondary generation of spray due to the fall of the droplets onto a rough surface. The general scheme of the experiments was similar to [1]. The range of equivalent wind speed from U10 is from 21 to 34 m/s. Initially, high-speed filming with shadow visualization of the rough surface from above was performed, followed by detection, marking and calculating the number of events during image processing using special programs. It has been demonstrated that the number of phenomena of falling drops into water per unit time per unit area, leading to the formation of spray, significantly exceeds the similar values for previously studied mechanisms of spray generation: liquid ligaments fragmentation type, bubbles rupture and bag-breakup fragmentation type. Further, detailed studies of this phenomenon were carried out with a higher resolution filming. Two main scenarios of this event: with the formation of a “crown” at large angles of drop incidence, and the so-called "jet" at small angles were identified by analogy with [2]. The number droplets, size and velocity distributions were obtained for different wind speeds. These results can be used to design a spray generation function due to this phenomenon.

Investigations were supported by Russian Science Foundation project 21-19-00755 (carrying out experiments), Russian Foundation Basic Research project 21-55-52005 (data processing), work of A.A. Kandaurov was partially supported by the President's grant for young scientists МК-5503.2021.1.5.

Troitskaya, A. Kandaurov, O. Ermakova, D. Kozlov, D. Sergeev, and S. Zilitinkevich, The ‘Bag Breakup’ Spume Droplet Generation Mechanism at High Winds. Part I: Spray Generation Function, J. Phys. Oceanogr., vol. 48, no. 9, pp. 2167–2188, 2018.

V. Gielen, P. Sleutel, J. Benschop, M. Riepen, V. Voronina, C. W. Visser, D. Lohse, J. H. Snoeijer, M. Versluis, andH. Gelderblom, Oblique drop impact onto a deep liquid pool, Phys. Rev. Fluids, vol. 2, pp. 083602, 2017.
Diagnose the diffractive contribution to GNSS scintillation at high latitude during the geomagnetic storm on 7-8 September 2017
Authors: Xiong Chao , Zheng Yuhao , Jin Yaqi , Liu Dun , Xu Chunyu , Zhu Yixun , OKsavik Kjellmar
Published: March 2022

The ionospheric plasma irregularities can cause severe scintillation of the trans-ionospheric radio waves, e.g., signals from the global navigation satellite system (GNSS). The phase scintillation of GNSS signal are usually caused by both refractive and diffractive variations, while the amplitude scintillation is mainly attributed to diffractive process. At high latitude, the GNSS signals usually exhibit strong phase scintillation, but the meanwhile amplitude scintillation is very low. Such a feature leads to the commonly known issue as “phase without amplitude scintillation at high latitude”. In this study, we focused on the geomagnetic storm happened on 7-8 September 2017. High-resolution data from four GNSS receivers at high latitudes were utilized. Quite intense phase and amplitude scintillations, represented by σ4 and S4, respectively, were observed during the storm mainly phase. By checking the ionosphere-free linear combination (IFLC) parameter, the intense phase and amplitude scintillations are found associated with diffractive effects. Simultaneous observations from the Swarm satellite have been further analyzed to resolve the possible reasons that cause the diffractive influence of scintillation. 
Observing Extreme Rainfall Events at Fine Timescales
Authors: Chou Ching-Chun , Wang Li-Pen
Published: March 2022

The Computational Hydrometeorology Lab in National Taiwan University (NTU CompHydroMet Lab) recently launched a rainfall monitoring network, with a special focus on observing extreme storm events, such as typhoons and thunderstorms, over the south area of Taipei. Due to the topographic effect and the constant humidity brought by the sea breeze, together with the high temperature, south Taipei is a hotspot for the occurrence of thunderstorms in summer. The monitoring network constitutes a collocated pair of an OTT Pluvio S and an OTT Pluvio L weighing rain gauges, as well as two ‘unconventional’ rain sensors – an OTT Parsivel2 disdrometer and a Lufft WS100 radar precipitation sensor. These rain sensors are co-located within a 10 x 10 m2 area, providing rainfall estimates at high temporal resolutions, ranging from 10 seconds to 1 minute.

Since the launch of the monitoring network in March 2021, the monitoring network has collected rainfall data for two typhoons and a number of thunderstorms, with the highest peak intensity at 245.6 mm/h. The measurements are generally consistent between four sensors; in particular, those from two weighing gauges are of the highest consistency. In addition, a  preliminary comparison shows that the high-intensity rainfall measured by weighing gauges and disdrometer are in high agreement. This suggests that weighing gauges –which were widely used as a verification gauge for the tipping bucket gauges in the operational context–  can provide reliable rainfall measurements with high accuracy, including capturing extreme rainfall. 
 
As compared to other sensors, WS100 tends to underestimate rainfall at high intensities. However, it is more sensitive to low-intensity rainfall than others; and, similarly to the disdrometer, it provides reflectivity data and requires less maintenance. The cause of underestimation is currently under investigation, which could potentially be improved through the calibration of the current algorithm with weighing gauges’ measurements. 

At the next stage of the work, these ground measurements will be compared with the coincidental three-dimensional radar data product from the Central Weather Bureau (CWB), Taiwan. The radar data product from CWB is available at approximately 1.2 km spatial resolution and 10-min intervals. The comparison result will be presented, and the potential of using the monitoring network to support the correction of radar data will be discussed.
Condition for the formation of the Mozambique ridge (physical modelling)
Authors: Tolstova Anastasiia , Dubinin Evgene , Grokholsky Andrey
Published: March 2022

The Mozambique Ridge is located in the southwestern Indian Ocean between
two Mesozoic ocean basins: the Natal Basin and the Mozambique Basin. The
Mozambique ridge is formed from several bathymetric plateaus rising to 3500 m from
the seabed. It is believed that the origin of the ridge is associated with its partial
separation from the outskirts of the African continent due to the activities of the Karoo
hotspot. Recent studies show that the northeastern part of the ridge is thinned
continental crust covered with sediments, and the southern part is characterized by a
large number of extrusion centers indicating increased igneous activity. Experimental
studies described in this work showed that the formation of the Mozambique ridge
occurred in the context of the destruction of the Afro-Antarctic continent with
structural heterogeneities in the lithosphere of the African continent and the influence
of the Karoo hotspot.
This work was supported by the Russian Science Foundation
(project no. № 22-27-00110).
Optimization of calibration models for soil carbon estimation using mid-infrared spectroscopy
Authors: Dorantes Minerva , Fuentes Bryan , Miller David
Published: March 2022

Mid-infrared spectroscopy is an efficient technique for soil carbon analysis. Efforts to measure and monitor carbon through mid-infrared spectroscopy require the development of soil spectral libraries. These libraries are used for the construction of calibration models which relate analyte values to spectra. The optimization of these models is an important process for the accurate and resource-efficient estimation of soil carbon. This study demonstrates the effect on model performance of subsetting a soil spectral library for soil organic carbon estimation. Various subsetting criteria were tested across different landscapes in the United States, and results are presented in the context of the development of new soil spectral libraries.
Contribution of Asian emissions to upper tropospheric CO over theremote Pacific
Authors: Smoydzin Linda , Hoor Peter
Published: March 2022

By analysing the global distribution of the highest 2% of daily CO mixing ratios at 400hPa derived from the MOPITT satellite instrument for 20 years (2000-2019), we detect very regularly regions with very high CO values (i.e. mixing ratios belonging to the globally highest 2%) over the remote northern hemispheric (NH) Pacific. Such events of elevated CO over the upper tropospheric NH-Pacific occur throughout the year, with a surprisingly high regularity and frequency (70% of all days during winter, 80% respectively during spring).

During winter, most of these pollution events are detected over the north-eastern and central NH-Pacific, during spring over the central NH-Pacific and during summer over the western NH-Pacific. We detect most pollution events during spring. To link each individual pollution event detected by the 2% filtering method with a specific CO source region, we performed trajectory calculations using MPTRAC, a lagrangian transport model. To analyse transport pathways and uplift mechanisms we combine MOPITT data, the trajectory calculations and ERA-Interim reanalysis data. It becomes apparent, that air masses from China being lifted along a frontal system into the free troposphere are the major CO source throughout the year. The contribution of other source regions and uplift mechanisms shows a strong seasonal cycle: NE-Asia in relation with upward transport of air masses in the warm conveyor belt of a midlatitude cyclone is a significant CO-source region during winter, spring and summer while India is an important source region mainly during spring and summer and SE-Asia mainly during spring.
ML-based Probabilistic Prediction of 2m Temperature and Total Precipitation
Authors: Zaytar Mohamed Akram , Zadrozny Bianca , Watson Campbell , Salles Civitarese Daniel , Eben Vos Etienne , Michael Mathonsi Thabang , Lukhetho Mashinini Thabang
Published: March 2022

The need to build reliable weather forecasting systems for subseasonal to seasonal (S2S) timescales has never been greater as the world continues to experience increased numbers of extreme weather events. This study addresses the skill gap between numerical weather prediction (NWP) and seasonal forecasting by proposing a daily probabilistic forecast model that predicts 2-meter temperature and total precipitation on a global scale. It combines multimodal data (e.g., physics-based ensembles, climate modes, recent climatology) into feature vectors given as inputs to three ML models: Extreme Gradient Boosting, U-Net, and Natural Gradient Boosting. We use Bayesian hyperparameter search and leave-one-year-out RPSS cross-validated scores to accelerate learning and ensure generalizability. Our method consistently outperforms both ECMWF 46-day forecasts and climatology. We find that augmenting physics-based issued forecasts with other sources of predictability greatly improves the performance of the underlying dynamical models. We hope that by improving the physics-based probabilistic forecasts, we will unlock skill in predicting climate extremes-oriented indices. Subsequent representation learning models may be trained to efficiently navigate the ensembles' uncertainty space and estimate the likelihood of extreme events.
Optimal sowing dates for major crops in India under climate change
Authors: Sharma Aditya Narayan , Gaddam Sai Jagadeesh , Sampath Prasanna Venkatesh
Published: March 2022

Agriculture plays a pivotal role in supporting the socioeconomic situation of millions of farmers in India, which is increasingly coming under threat due to climate change. In particular, the future changes in rainfall patterns has the potential to directly affect the irrigation water demands, thereby impacting water consumption, agricultural productivity, and influencing food security. For instance, the optimal sowing dates for crops may change according to the altered rainfall patterns. With this motivation, we studied the impacts of shifts in sowing periods in order to identify the optimal sowing dates for a particular crop. First, we collected daily temperature and rainfall data for India at a resolution of 0.25o from different GCM models (EC-Earth 3 and EC-Earth 3 veg) under different SSP scenarios (SSP 126, SSP 245, SSP370, SSP585). Also, region-wise agricultural data such as crop acreage and sowing dates were collected for seven major crops - paddy, wheat, maize, groundnut, sugarcane, red gram, black gram, and soybean. Subsequently, we estimated the reference evapotranspiration using the modified Penman-Monteith method. The estimated reference evapotranspiration and rainfall data were incorporated into FAO’s CROPWAT model to calculate the irrigation water requirements (IWR) of the selected crops. The optimal IWR for each crop was selected by varying the sowing dates at fifteen-day intervals across the year (twenty-four dates for the year). Preliminary results indicate that there is considerable scope for water savings by shifting the sowing dates of staple crops to account for climate change impacts. These strategies may become vital for policymakers in the coming decades to reduce the stresses on water without endangering food security. Indeed, such strategies require the cooperation of various stakeholders for better implementation at multiple scales.
Terrestrial net primary productivity dynamics under climatic variability and urban expansion in western Himalaya
Authors: Sharma Sonali , Joshi Pawan Kumar , Fürst Christine
Published: March 2022

The western Himalaya is one of the most climate-sensitive and ecologically vulnerable ecosystems of the world. In the recent past, the region has undergone rapid alterations owing to climate change and paced urbanization. These alterations have significantly impacted Terrestrial Net Primary Productivity (TNPP) of the region. The present study takes the emerging urbanizing centers: Pithoragarh (Uttarakhand) and Dharamsala (Himachal Pradesh), situated in Indian western Himalaya to estimate TNPP dynamics of various land use classes. The study demonstrates usage of Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) for predicting a high spatio-temporal Normalised Difference Vegetation Index (NDVI) imagery obtained by fusing spatial details of Landsat NDVI and temporal details of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images. The predicted NDVI showed a good agreement with actual Landsat NDVI (R2=0.64 and 0.89; RMSE: 0.09 and 0.04; p < 0.01 for Dharamsala and Pithoragarh, respectively), therefore was reliable for TNPP estimations. This was assimilated in Carnegie Ames Stanford Approach (CASA) model for TNPP estimation for the years 2001 to 2019. The preliminary results show a net loss in TNPP in both of the urbanizing centers. During the study period 2001-2019, TNPP fluctuated annually and showed a decreasing trend of 1475.77 g C m-2 year-1 and 790.84 g C m-2 year-1 in Dharamsala and Pithoragarh, respectively. Among the forest vegetation classes, Oak the most dominant forest class experienced the highest decline in TNPP accounting for 67.55% and 34.04% of net TNPP loss in Dharamsala and Pithoragarh, respectively. The urban expansion contributed to 14.77% (Dharamsala) and 9.77% (Pithoragarh) decline of net TNPP loss. The results provide a better understanding of spatio-temporal dynamics of TNPP consequent to climatic variability and urbanization and provide a theoretical reference for future urban planning.
P- and S-wave arrival picking and epicentral distance estimation of earthquakes using convolutional neural networks
Authors: Choi Yonggyu , Bae Sungmyung , Song Youngseok , Seol Soon Jee , Byun Joongmoo
Published: March 2022

In recent years, machine learning techniques have been widely applied in seismological data processing such as seismic event detection, phase picking, location, magnitude estimation, and further data analysis for determining source mechanisms. Especially in earthquake location, deep learning methods are used to reduce location errors compared to conventional algorithms.

In this study, we present a deep learning based epicentral distance estimation with two separate models using seismic data from two stations as input data. The first model is the P- and S-wave arrival time picking model and the second is the epicentral distance estimation model. Since the traditional epicentral distance estimation methods uses the difference in arrival times between P- and S-waves, the P- and S-wave arrival times were first predicted from three-component seismic data so that this information could be directly used as the next input data. This picking information is used as input data along with the station location in the epicentral distance estimation model to output the final epicentral distance. Since this method uses data from two stations, it has higher accuracy than epicentral distance estimation using data from a single station.

The P- and S-wave arrival time picking model was modified by referring to the ResUNet (Diakogiannis et al., 2020) structure to improve performance based on the seismic detection and phase picking model from the three-component acceleration data developed by Mousavi et al. (2020). This modified model performs feature extraction for P- and S-phase picking and includes a residual block and skip connection. The model for estimating the distance from the epicenter was constructed using a basic artificial neural network (ANN) architecture. As input data, a total of eight features were used by adding six combinations of the difference in arrival times of P-wave and S-wave in each component of the two stations and two values of the latitude and longitude difference between two stations. The ANN architecture consists of four hidden layers and the epicentral distances of the two stations are final output.

The STEAD data were used as training data and test data. The STEAD is a seismogram dataset recorded from about 450,000 global earthquakes, and among them, data with magnitudes greater than 2.5 and epicentral distances less than 400 km were selected and used. As a result of applying the trained model to the test data, the mean absolute error of the predicted epicentral distance was 6.5 km, which showed improved performance compared to the previous results. Also, since this method uses six time-differences as input data, it can provide more robust results even in the presence of random noise at the picked times.
Snow melt triggering of shallow landslides under climate change. The case study of Tartano valley, Italian Alps.
Authors: Chiarelli Davide Danilo , Bombelli Giovanni Martino , Bocchiola Daniele , Rosso Renzo , Rulli Maria Cristina
Published: March 2022

Shallow landslides (SLs) imply downhill movements of soil, rocks, debris. These typically occur on steep terrains, in mountainous, and hilly areas, representing a major risk for people and infrastructures. Properly mapping of shallow landslides in space and time is fundamental for prediction, forecast, and setting up of countermeasures. However, modelling of shallow landslides is complex, given (very) local nature of the phenomenon. Recently investigation started about the role of snow melt in triggering shallow landslides, displaying increasing evidence of catastrophic events at thaw. Little was done hitherto in modelling snow melt triggered SLs, especially in terms of physically based modeling. Under the umbrella of the recent project MHYCONOS, funded by Fondazione CARIPLO of Italy, we developed a robust, and parameter-wise parsimonious model, able to mimic triggering of SLs accounting for the combined effect of precipitation duration and intensity, and snowmelt at thaw. In our model, when temperature is below 0 °C, precipitation is stored as snowpack on the soil surface, and released later in thaw season. Storage of melting water during springtime increases soil moisture, so creating potential for SLs. The model is demonstratively applied to the Tartano river valley, in the Alps of Lombardia region of Italy. In this region mass movements and flash-floods in the wake of intense storms are common. Currently from our model about 26% of the Tartano valley displays (permanent) unstable conditions, more than 40% of it influenced by soil moisture changes. Conversely, by applying a traditional rainfall-based analysis, only 19% of the basin is predicted as potentially unstable, mainly in fall, when intense rainfall occurs. When including snowmelt as a cause of SLS triggering, one finds anticipation of the (modeled) peak of instability to springtime, during April and May. Forcing the model under 6 different climate change scenarios of IPCC at 2050, and 2100, an increase is expected in temperature (i.e. with rapider snow melt), and extreme precipitation events, further aggravating SLs hazard. Mapping zones prone to instability in space and time under present conditions, and future scenarios, will help to prevent casualties, and damages in the short-term, while providing base for structural mitigation measures in the long term, during periods of potential instability, even at low to medium rainfall rates.
Deflection effect in the interaction between granular flow and semi-ellipsoid obstacle array
Authors: Yu Wangxin , Yang Su , Wang Xiaoliang , Liu Qing-quan
Published: March 2022

Granular flow impacting structures is an important problem in the research of providing scientific basis for disaster prediction and mitigation, so it is of great significance to deepen the understanding of the interaction law. We studied the spread and deposit behaviors of fast granular flow impacting an array of semi-ellipsoid obstacles with different parameters such as the height, distribution density and deflection angle. It is found that the flow and deposit state of granular matter are controlled by the obstacle array through both dissipation and deflection effect. We quantified the deposit behavior by two dimensionless indices, one pre-existing index called runout efficiency, and a new proposed index termed as deflection efficiency. This work would provide help in designing protective obstacle arrays by exploring the relationship between regulation effect and parameters of the obstacle array.
‘Dancing around the Östrem curve’: High-resolution monitoring of a supraglacial rockslide on Brenndalsbreen, Norway
Authors: Seier Gernot , Abermann Jakob , Engen Siri H. , Gjerde Marthe , Scheiber Thomas , Löffler Karina , Carrivick Jonathan L. , Andreassen Liss M. , Yde Jacob C.
Published: March 2022

Landslides or rockslides occur in unstable slopes around the world, of which many cases are related to changes in the cryosphere. They can result in natural hazards and are an indicator of climate change. Due to their inherent non-linearity, they are difficult to predict and often remain unobserved. Appropriate documentation allows for assessing their consequences and long-term impacts on ecosystems or the hydrological cycle.

We report on a particularly well-documented case of a supraglacial rockslide that occurred on Brenndalsbreen, an outlet glacier of Jostedalsbreen, Norway, in the period November 2009-June 2010. We assess its consequences on local glacier mass balance derived from surface elevation changes and explore potential changes in flow velocities. The rockslide occurred unobserved and did not obviously impact humans or the environment, yet, satellite imagery and aerial photogrammetrical surveys allow for a spatial and temporal quantification of the event. Furthermore, a series of digital elevation models from 2012-2021 is used to determine spatial heterogeneity in ablation rates, however, this is complicated due to the motion of the ice mass. According to the widely used Östrem curve, a debris-cover exceeding a certain threshold thickness protects the ice below from ablation, while a thin debris or dirt layer increases ablation rates. In fact, we find that during the first years after the rockslide, locally, ablation was reduced below the debris layer, while a recent high-resolution LiDAR survey that got complemented with a UAV survey a year later, clearly indicates increased ablation rates relative to the debris-free surroundings. Clear trends in surface velocities have not been found based on satellite remote sensing data. We discuss the significance of the observations on surface energy balance and geomorphological changes in the proglacial area.
Use of vegetation indices for irrigation management in commercial vineyards
Authors: Camacho Gema , Hueso Antonio , Mendoza Giancarlo , Ramos Julián , Tarquis Ana , Gómez del Campo Maria , Baeza Pilar , López Juan
Published: March 2022

Water availability in vineyards plays an integral role in the sustainability of high-quality grapes and prevention of devastating crop loses. Stem water potential (Ψstem) reflects consistently vineyard water status, serving as an aid in irrigation management. However, some drawbacks make the Ψstem little used in commercial vineyards. It requires a pressure chamber, contracting a gas supplier, the need for one or two technicians to carry out the measurement, and the small size of the sample obtained limits the use to control large areas that normally present high intra-field variability.

The objective of this work was to establish a relationship between the Ψstem and the hyperspectral vegetation indices. Four irrigation doses were imposed in a commercial vineyard. Ψstem was measured five days during three-time intervals a day in 2019. The data for the calculation of vegetation indices can be taken quickly by means of a multispectral camera mounted on a UAV, be recorded and processed later. Two different indexes were calculated: NDVI and TCARI/OSAVI.

A total of 12 flights have been made, in addition to 320 measured data for the Ψstem, on 5 different dates at three different time intervals (morning, noon and afternoon) during 2019.There was a significant linear correlation (R2 = 0.69, P <0.001) between the TCARI / OSAVI and the Ψstem. Despite the fact that the most widely used is the NDVI, in this study, the TCARI/OSAVI has obtained a tighter adjustment in all cases than NDVI.

The relationship allows estimating the Ψstem from the index (TCARI/OSAVI), which allows the knowledge of the vineyard water status of a larger area, improving the irrigation management in a more functional way for commercial plantations.

ACKNOWLEDGEMENTS

Grant AGL2016-77282-C3-2R funded by MCIN/AEI/ 10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union NextGenerationEU/PRTR”.

This work has been possible thanks to Licinia Wines (Morata de Tajuña, Madrid).

REFERENCES

Cancela, J.J.; Fandiño, M.; Rey, B.J.; Dafonte, J.; González, X.P. (2017). Discrimination of irrigation water management effects in pergola trellis system vineyards using a vegetation and soil index. Agricultural Water Management 183, 70-77. https://doi.org/ 10.1016/j.agwat.2016.11.003

Espinoza, C.Z., Khot, L.R., Sankaran, S., Jacoby, P.W. (2017). High resolution multispectral and thermal remote sensing based water stress assessment in surface irrigated grapevines. Remote Sensing, 9 (9). https://doi.org/10.3390/rs9090961
The connection between quality of wind forecasts and the dynamics of the winds
Authors: Ólafsson Haraldur
Published: March 2022

Motivated by the need to predict dust-storms, a large set of wind observations are compared to 24 h point forecasts with a high-resolution numerical model.  The cases are classified according to the dynamic nature of the winds; (a) wind over flat land, (b) enhanced winds blowing along a mountain (barrier/corner winds) and (c) downslope winds. The mean quality of the forecasts over flat land is similar to the quality of the forecasts of enhanced winds blowing along a mountain.  The quality of the forecast of the downslope winds is much poorer than the quality of the forecast of winds over flat land and winds blowing along a mountain.  In the downslope case, it is up to ten times more likely to either miss a windstorm or to forecast a windstorm that does not occur, than if the winds are not downslope.
Unravelling user requirements for climate services to support water allocation decisions in a drought prone region in Spain
Authors: Ramos Sánchez Celia , De Stefano Lucia , Werner Micha , van Andel Schalk Jan
Published: March 2022

The development of climate services to address water-related challenges is rapidly progressing. Users’ engagement and adequate interaction between climate service providers and climate service consumers are recognized factors favouring climate services uptake among and the tracking and evaluation of their socio-economic benefits. Under the framework of the H2020 CLINT project, this research presents a preliminary analysis of user requirements in the Douro River Basin (Spain) to develop a climate service prototype, enhanced with artificial intelligence (AI), and intended to assist in the water allocation process; thereby improving drought adaptation. Participatory modelling is applied to explore the full decision-making process among competing needs, namely the allocation of water resources to environmental flows and irrigation. The research focuses on how different actors use historical and forecast hydrometeorological information to inform drought early warning and water use planning (e.g. user-inspired drought indicators), understanding how uncertainty influences decisions, the role of other types of information in the decision-making process, and the differing perceptions of droughts and drought impacts. These assessments of local user knowledge and the need for enhancements are key inputs to further develop and evaluate AI-enhanced climate service in the following stages of the CLINT project.
Re-designing the urban water cycle: Towards Water-Age-Neutral Habitats
Authors: Benettin Paolo , Barcelloni Corte Martina , Wehrle Cédric , Soulsby Chris
Published: March 2022

Knowledge of how to articulate the “urban transition” is today urgently needed. Urbanization is on a steadily growing trend that impacts the water cycle as a whole. However, while the effects of urbanised/urbanising areas on water quantity (how much water) have been well studied for flood prevention, other effects –as those related to water quality (which water)– are less known. Taking hold from the most recent developments on the “water age” concept, i.e. the time that water resides in the landscape before exiting as runoff or evaporation, we propose a proof-of-concept study on the notion of “water-age-neutral” design. This concept envisions the possibility of lowering –through design– net impacts on the City-Territory’s “natural” water age balance. To do this, we selected 4 representative areas of 250x250 meters within the Panke watershed, in the metropolitan area of Berlin (DE), which are characterized by specific land-use/urban form patterns (industry, single family housing, residential slabs and residential open block housing). For these 4 areas, we used an ecohydrological model to analyse a set of water/land use interaction patterns and their outputs in terms of water flow partitioning and water age. We use such outputs to evaluate the broader impacts of land-use/urban form on the urban water cycle. These results are considered as a first step towards a larger evaluation of the multiple relationships between land-use/urban form and the water cycle as a whole.
CHarm: C library to work with spherical harmonics up to almost arbitrarily high degrees
Authors: Bucha Blazej
Published: March 2022

Spherical harmonic transforms aiming at degrees as high as a few tens of thousands are vital in geodesy to improve our knowledge of the Earth's gravity field.  A prominent example is spectral gravity forward modelling of topographic masses, which is able to approximate fine gravity field structures up to the sub-km-level and beyond (degree ~20,000 and higher).  Driven by these applications, we have developed CHarm, a C library to perform spherical harmonic transforms.  CHarm is centered around (but not limited to) high-degree expansions, say, well beyond degree 2700.  Its goal is to be numerically stable on the one hand, while achieving reasonable computational efficiency with minimized memory requirements on the other hand.  Supported are surface spherical harmonic analysis and solid (3D) synthesis, both with point and area-mean data values.  Standard quadratures due to Gauss--Legendre and Driscoll--Healy are implemented for exact harmonic analysis of point data values.  The library can be compiled in double precision or, in case higher numerical accuracy is sought, in quadruple precision.  For efficient FFT transforms along the latitude parallels, the state-of-the-art FFTW library is employed to boost the performance.  Unique to CHarm is a routine integrating solid spherical harmonic expansions on band-limited undulated surfaces.  It can deliver, for instance, area-mean potential values on planetary surfaces.  Available are also routines to compute Fourier coefficients of Legendre functions and integrals of a product of two spherical harmonics or of two Legendre functions over a restricted domain.  To utilize the power of multicore processors, CHarm can be compiled with enabled parallelization on shared-memory architectures (OpenMP).  A significant effort is put into the documentation of the library (HTML, PDF) to allow its easy use.

In this contribution, we discuss the motivation behind the development of CHarm, explain its main functionalities and demonstrate some usage case studies.  Within a high-degree closed-loop synthetic environment, we assess the numerical accuracy, the computational speed and the memory management of the library.  A discussion on the future work closes the contribution.  CHarm is available at https://edisk.cvt.stuba.sk/~xbuchab/charm/doc/index.html.
Unsupervised clustering of Lagrangian trajectories in the Labrador Current
Authors: Planat Noémie , Jutras Mathilde
Published: March 2022

Lagrangian studies are a widely-used and powerful way to analyse and interpret phenomenons in oceanography and atmospheric sciences. Such studies can be based on dataset either consisting of real trajectories (e.g. oceanic drifters or floats) or of virtual trajectories computed from velocity outputs from model or observation-derived velocities. Such data can help investigate pathways of water masses, pollutants or storms, or identify important convection areas to name a few. As many of these analyses are based on large volumes of data that can be challenging to examine, machine learning can provide an efficient and automated way to classify information or detect patterns.

Here, we present an application of unsupervised clustering to the identification of the main pathways of the shelf-break branch of the Labrador Current, a critical component of the North Atlantic circulation. The current flows southward along the Labrador Shelf and splits in the region of the Grand Banks, either retroflecting north-eastward and feeding the subpolar basin of the North Atlantic Ocean (SPNA) or continuing westward along the shelf-break, feeding the Slope Sea and the east coast of North America. The proportion feeding each area impacts their salinity and convection, as well as their biogeochemistry, with consequences on marine life.

Our dataset is composed of millions of virtual particle trajectories computed from the water velocities of the GLORYS12 ocean reanalysis. We implement an unsupervised Machine Learning clustering algorithm on the shape of the trajectories. The algorithm is a kernalized k-means++ algorithm with a minimal number of hyperparameters, coupled to a kernalized Principal Component Analysis (PCA) features reduction. We will present the pre-processing of the data, as well as canonical and physics-based methods for choosing the hyperparameters. 

The algorithm identifies six main pathways of the Labrador Current. Applying the resulting classification method to 25 years of ocean reanalysis, we quantify the relative importance of the six pathways in time and construct a retroflection index that is used to study the drivers of the retroflection variability. This study highlights the potential of such a simple clustering method for Lagrangian trajectory analysis in oceanography or in other climate applications.
The quest for the missing plastics:  Large uncertainties in global river plastic export into the sea
Authors: Laufkoetter Charlotte , Roebroek Caspar T.J. , González-Fernández Daniel , van Emmerik Tim
Published: March 2022

Plastic pollution in the natural environment is causing increasing concern at both the local and global scale. Understanding the dispersion of plastic through the environment is of key importance for the effective implementation of preventive measures and cleanup strategies. Over the past few years, various models have been developed to estimate global river plastic export into the oceans, using limited plastic observations in river systems. However, there is a large discrepancy between the amount of plastic being modelled to leave the river systems, and the amount of plastic estimated to be in the ocean.  Here we perform a careful uncertainty analysis of the riverine plastic export estimates, examining both observational uncertainty, model parameter uncertainty, and underlying assumptions in models. Among the quantifiable sources of uncertainty, the conversion of visual plastic observations to plastic mass estimates introduces the largest uncertainty, leading up to three orders of magnitude uncertainty in the final mass estimates in most observations. Model structure and parameter uncertainty add an additional order of magnitude of uncertainty. Additionally, most global models assume that variations in the plastic flux are primarily driven by river discharge. However, we show that within the largest currently available datasets, correlations between river discharge (and other environmental drivers) and the plastic flux are never above 0.5, and strongly vary between catchments. Overall, we conclude that the yearly plastic load in individual rivers as well as the global riverine plastic flow into the ocean may be substantially less well-constrained than indicated by previous studies.
Hydrological impacts of contrasting gully blocking techniques for peat restoration and natural flood management in a degraded blanket peatland 
Authors: Allott Tim , Edokpa Donald , Cummings Thea , Evans Martin , Shuttleworth Emma , Milledge David , Kay Martin , Johnson Adam , Howson Tim , Rees Joe , Spencer Tom
Published: March 2022

Erosion gullies are a common feature of degraded blanket peatlands and in recent years gully blocking has been increasingly employed as a restoration approach. The stated aims of gully blocking are typically to stabilise eroding gullies and to rewet the adjacent peatland by raising water tables, but in recent years the potential benefits of gully blocking for natural flood management (NFM) have also been recognised. However, data on the hydrological effects of gully blocking and for different gully blocking techniques are sparse.

We report on a before-after-control-intervention (BACI) experiment of gully blocking in peatland micro-catchments (hectare scale) in the Peak District National Park, UK. Three different gully blocking interventions were made in March 2020: impermeable peat dams, permeable cobble dams, and peat dams with a restricted diameter bypass pipe. The first two interventions represent standard restoration techniques, whereas the piped peat dams are specifically designed to be optimal for natural flood management benefit. The micro-catchments were monitored for one year before and two years after gully blocking for: rainfall, discharge, depth to water table proximate to the gullies (within 2m) and depth to water table distal from the gullies (>10m away). Storm hydrograph data (peak discharges and lag times) were extracted for >120 storms from the rainfall-runoff data. All intervention data were analysed relative to data from a control micro-catchment.

After blocking for all interventions there were significant declines in median depth to water table proximate to the gullies, with the magnitude of the rewetting benefit in the following order: peat dams > piped peat dams > cobble dams. There were no significant changes in depth to water table at the distal locations. Peat dams led to a slight increase in storm peak flows but no change in hydrograph lag times. Stone dams led to no change in peak flows but increases in lag times. Piped peat dams resulted in the greatest changes to storm hydrographs, with substantial declines in peak flows and increases in lag times once the pipe diameter had been optimised to the discharge regime.

Peat dams maximise the rewetting benefits of gully blocking but appear to have limited NFM potential, whereas once optimised, piped peat dams provide maximum NFM benefit and greater water table recovery than stone dams.  These findings are important for restoration practitioners when making decisions on which gully blocking techniques to employ to balance the co-benefits of peatland restoration.
Variations in organic carbon and mineral composition of surface sediments in different areas of the East Siberian Arctic shelf
Authors: Gershelis Elena , Poltavskaya Natalina , Klevantseva Elizaveta , Shaldybin Mikhail , Smirnova Natalia , Chernykh Denis , Ruban Alexey , Dudarev Oleg , Gustafsson Örjan , Charkin Alexander , Semiletov Igor
Published: March 2022

Ongoing climate warming in the Arctic accelerates mobilization of freeze-locked carbon from the coastal and subsea permafrost deposits, affecting transport, accumulation and transformation of organic carbon (OC) in the permafrost-dominated land-shelf system of the Eastern Arctic. The mineral matrix is believed to exert a first-order control on OC degradation state and thereby on its potential degradation and release as CO2 and CH4 to the atmosphere. To understand the interactions between organic matter and mineral composition, we investigated four sediment cores obtained from contrasting sedimentation areas: coastal (Buor-Khaya Bay, BK; 20 cm) and outer part (LS; 17 cm) of the Laptev Sea, Dmitry Laptev Strait (DLS; 14 cm), Chaunsky Bay (CB; 14 cm) of the East Siberian Sea.

This study presents preliminary results of the sedimentary organic matter composition analyzed with both Pyrolysis-GCMS and Rock Eval pyrolysis, along with the clay mineral composition and grain size distribution. By combining these mineralogical and macromolecular data, we aim to get new insights into the interactions between the mineral matrix and OM and evaluate how the physical properties of the sediments affect the OM fate in the different areas of the East Siberian Arctic shelf.

The variability in the OC and mineral composition observed for the different sampling sites, as well as for different sampling horizons, reflects the dominance of diverse hydrodynamic regimes. The greatest variations with sampling depth were observed in sediments of the DLS system characterized by transit/transport bottoms. DLS sediments were generally enriched in aromatics (up to 44.6%), lipids (up to 34.2%) and lignin markers (up to 34.3%). A relatively uniform distribution is observed for the samples from CB, a predominantly accumulative system with a semi-closed water exchange, where proportions of lipids and lignin markers where also high. Rock Eval pyrolysis also revealed significant variability between the studied sites: hydrogen index values generally were within the range of 74-164 mg HC/g TOC with higher average values in the CB samples. Oxygen index values were between 150 and 241 CO2/g TOC: the highest values were observed in the BK sediments. The average clay mineral contents in the studied sediment cores vary as follows: illite from 46.1 % in DLS to 58.7 in BK; illite-montmorillonite from 6.6% in CB to 17.6% in DLS; montmorillonite from 3.1% in CB and BK to 5.3% in LS; chlorite from 6.5% in CB to 9.2% in LS; kaolinite from 19.7 % in LS to 29.7 % in CB.

Analytical studies were financially supported by RSF (Project # 21-77-00075). Research cruises were also performed with support of RSF (Project # 21-77-30001).
Characterising recent drought events using current reanalysis and remote-sensing soil moisture products with a standardised anomalies-based framework
Authors: Hirschi Martin , Crezee Bas , Dorigo Wouter , Seneviratne Sonia I.
Published: March 2022

Drought events have multiple adverse impacts on environment, society, and economy. Monitoring and characterising such events is thus crucial. Here we test the ability of selected current reanalysis and merged remote-sensing products to represent major seasonal and multi-year drought events of the last two decades globally. We consider the ERA5, and the related ERA5-Land, as well as the MERRA-2 reanalysis products, and the ESA CCI, and the corresponding near-real time C3S remote-sensing soil moisture products (both encompassing an ACTIVE, a PASSIVE and a COMBINED product). The considered products offer opportunities for drought monitoring since they are available in near-real time.

We focus on soil moisture (or agricultural) drought and analyse events within pre-defined spatial and temporal bounds derived from scientific literature. Based on standardised daily anomalies of surface and root-zone soil moisture, the drought events are characterised by their magnitude, duration, spatial extent, and severity (i.e., the combination of duration and standardised anomalies below -1.5).

All investigated products are able to indicate the investigated drought events. Overall, responses of surface soil moisture are often strongest for the reanalysis products ERA5 and ERA5-Land and weakest for the remote-sensing products (in particular for the ACTIVE satellite products). The weaker drought severities in the remote-sensing products are related to shorter event durations as well as partially less pronounced negative standardised soil moisture anomalies. The magnitudes (i.e., the minimum of the standardised anomalies over time) are reduced in MERRA-2 and in the ACTIVE satellite products. Diverse global distributions of long-term trends in dry-season soil moisture may explain some differences in the drought responses of the products. Also, the lower penetration depth of microwave remote sensing compared to the top layer of the involved land surface models, as well as sensing issues of active microwave remote sensing under very dry conditions could explain the partly weaker drought responses of the remote-sensing products during the investigated events. In the root zone (based on the reanalysis products), the drought events often show prolonged durations, but weaker magnitudes and smaller spatial extents.