bluuebunny/crossref_metadata_2025 huggingface dataset:
Producing a High-Resolution Land Cover Map for Southwest Ethiopia Using Sentinel-2 Images and Google Earth Engine
Authors: Vahidi Mayamey Farzad , Ghajarnia Navid , Aminjafari Saeid , Kalantari Zahra , Hylander Kristoffer
Published: March 2022

Accurate knowledge of local land cover and land use and their changes is crucial for many different applications such as natural resources management, environmental studies, ecological and biodiversity change evaluations, and food security. Global landcover maps can be useful datasets as a reference source and starting points, however, they usually show areas of geographical disagreements when compared to one another. Moreover, the global land cover products mostly generalize different land cover types which may not fit exactly to the specific needs of different projects and user communities. For instance, different types of forests are mostly considered as one category as they are not easy to be differentiated. In this study, we used high-resolution time-series images of Sentinel-2 to produce a local land cover for southwest Ethiopia with focusing on 8 major land cover classes: Forests, Plantations of exotic trees, Woodlands, Home Gardens, Annual crop fields, Grazing Wetlands, Urban areas, and Open water bodies. We also utilized high-resolution google map satellite imagery and the local expert knowledge on the study area to produce an observational dataset for training and validating steps. Different machine learning algorithms, land cover combinations, and seasonal scenarios were also used to produce the best local land cover map for the study area. For this purpose, a two-step approach was implemented to produce the final high-resolution land cover map. Firstly, we produced the best individual maps for each landcover class based on the highest producer accuracy among different scenarios. Then to produce the final land cover map for all land cover classes, all individual maps were combined by using the consumer accuracy index. For this, we found the most accurate land cover class for each pixel based on the highest consumer accuracy across all individually produced maps in the first step. In the end, we evaluated the results by the validation dataset and using different confusion indices. The final high-resolution land cover map produced in this study showed us the combination of remote sensing and local field-based knowledge in cloud computing platforms like google earth engine (GEE) improves the mapping of different land cover classes across southwest Ethiopia.

 

Keywords: Land cover map; Sentinel-2; High resolution; Machine Learning; Google Earth Engine; Ethiopia
Implementing sectoral water usage in the Community Earth System Model for projecting future water resource availability
Authors: Taranu Ioan Sabin , Thiery Wim
Published: March 2022

Due to the combined effect of human-driven depletion and anthropogenic climate change, groundwater storage is decreasing across the globe. This trend will potentially have an adverse impact on future human socio-economic development, by increasing the frequency and duration of both hydrological and socio-economic droughts as well as generating inter-sectoral competition for limited water resources.

Large-scale modelling studies on changes in groundwater availability can be separated into two big families. First, hydrological impact models actively consider water usage across sectors but ignore land-atmosphere interactions by design. Second, Earth System Models consider two-way interactions between climate and groundwater resources, but almost never consider the anthropogenic water resource depletion, except in some cases for irrigation.

The goal of this study is to connect the expertise of these two families by implementing domestic and industrial water usage in the Community Earth System Model version 2. Using land-atmosphere coupled simulations, we will revisit previously computed trends in future groundwater availability by simultaneously accounting for climate change and anthropogenic water resource usage.
Recent and future trends of river runoff in the North-West Russia
Authors: Grek Elena , Kurochkina Liubov
Published: March 2022

The research is devoted to the identification of the patterns in spatio-temporal variability of river runoff characteristics in the North-West Russia. Based on long-term observation series, trends in the runoff characteristics were calculated as well as the patterns of their spatial variability were analyzed.

Within the framework of the work, the river runoff characteristics for 3 long-term periods were analyzed: the previous (before 1966), modern (1966-2019) and future periods (2022-2099).

The results of our study showed increase amount of cases with maximum runoff rainfall flood being higher than spring runoff from the end of 1980s.Estimates on expected changes in the hydrological regime under the implementation of RCP 2.6, 6.0 and 8.5 scenarios are presented. The most significant changes were detected in winter runoff and maximum spring and rainfall runoff. It is shown that an increase in winter runoff should be expected for the study area, as well as a decrease in the maximum water discharge of the spring flood. At the same time, according to scenarios 6.0 and 8.5, by the end of the century, the maximum annual discharge is likely to be observed during the period of rainfall floods.
Deterministic scenarios for seismic hazard assessment in the metropolitan area of San Jose, Costa Rica. First results of the Kuk-Ahpan Project.
Authors: Ornelas Agrela Adriana Fatima , Benito Oterino Belen , Franco Blanco Rebeca , García Lanchares Carlos , Marchamalo Sacristan Miguel , Alvarado Guillermo , Climent Alvaro , Montero Walter , Schmidt Victor
Published: March 2022

We present here the first results of the KUK-AHPAN Project: INTEGRATED REGIONAL STUDY OF STRUCTURE AND EVOLUTION 4D OF CENTRAL AMERICAN LITHOSPHERE. IMPLICATIONS IN SEISMIC HAZARD AND RISK CALCULATION). One of the main purposes of this project is to improve the knowledge of the seismic hazard in Central American countries, as well as the seismic risk in populations of the region.

An initial phase is addressed to define deterministic scenarios in the capital cities, giving the expected strong motion due to possible ruptures in local faults which may be critical for the risk of the population.

Preliminary results have already been found in the metropolitan area of San Jose (Costa Rica), affected by moderate-high seismicity due to a complex system of faults in the Valle Central in a local frame. In a regional context, the seismicity of the country is explained by the tectonic interaction between the Cocos and Caribbean plates.

We have identified three critical scenarios corresponding to events located in the Belo Horizonte, Rio Azul, and Cipreses faults. The strong motion for these scenarios has been estimated firstly in rock conditions, by application of different Ground Motion Prediction Equations. (GMPEs). In the second place, a microzonation map for San Jose is proposed, derived from data of isoperiods, lithology and other geotechnical information.  The amplification factor for the different soils has been extracted from NEHRP. Finally, we estimated the peak ground acceleration (PGA) and other spectral accelerations SA(T) including the local effects for each rupture scenario defined.

These deterministic scenarios will be compared with other results obtained with probabilistic approaches including modelization of faults in the definition of source models. A final goal is to improve the knowledge of the influence of the source models based on faults, not only in zones, in the hazard estimates.
Modeling Holocene paleoclimate of Konya basin and comparison with proxy data
Authors: Erdem Neriman , Arıkan Bülent
Published: March 2022

 

Konya closed basin, located on the Anatolian plateau, hosted plenty of cultures and civilizations throughout the Holocene. The abundance of archaeological settlements and the current ecological fragility of the basin have increased the scholarly focus on the region. The basin offers a long-term and multi-dimensional record of human-environment interactions that reflect social, environmental, political, and economic processes. Paleolimnology studies are significant to reconstruct the paleoclimate and the paleoenvironment of the region. Sediment cores obtained from the basin, which is known to be paleo lake formerly and its surrounding lakes, provide multiple proxy records. Although plenty of paleoenvironmental studies were conducted in the region, reaching a temporally and regionally homogenous and long-term dataset is not straightforward. First, this research aims to build a paleoenvironmental synthesis of the Konya Basin. Secondly, it aims to reveal the climatic changes in the region throughout the Holocene quantitatively. In this study, Macrophysical Climate Model (MCM) was run with thirty years of observation data from a total of 20 meteorological stations located in and around the study area. The model outputs were compared with the local proxy records (oxygen isotopes and pollen records) obtained from the lacustrine environments of the region. MCM is a heat-budget modeling method to precisely recognize the mean centers of high and low sea-level pressure systems that manage the weather and wind patterns at mid-latitudes. The MCM model allows us to predict meteorological parameters at the interval of 100 years from the present to 40,000 years ago. Preliminary findings from the MCM point to the wetter and warmer periods in the Early Holocene, similar to isotope proxies in the region. Towards the end of the Early Holocene, precipitation decreases, and the driest climatic conditions occur in the Middle Holocene. The model outputs confirm the cessation of the active alluvion process in the Middle Holocene, which was experienced due to the reduction in the seasonality of precipitation. It was seen that increasing trend in winter temperatures during the Holocene for analyzed stations. On the advancing parts of the research, the findings from this study will be used in an agent-based modeling platform to understand the complex human-environment interaction in the region.
 Tale of crustal evolution of western Dharwar craton in Paleo-to- Meso Archean time: Insights from trace elemental composition of detrital zircons of some selected quartzite units.
Authors: Mitra Anirban , Dey Sukanta
Published: March 2022

Despite the influence of several extrinsic parameters that inhibits the use of trace element composition of detrital zircon grains in inferring their host rocks, workers had overcome many related problems and particularly constrained zircon/bulk rock partition co-efficient at least for different granitoids, for example. Based on these kind of progress and few other fundamental works, we have tried to apply trace element composition of detrital zircon grains retrieved from some basal quartz pebble conglomerate units and orthoquartzites of Dharwar craton in studying the crustal evolution pattern of this craton, specifically in terms of its changing crustal thickness with time. In this study, after categorising the pristine zircon grains identified by their La>1, Pr>1 and LREE-I<30 values, the values of their LREE/HREE ratio (measured by their Lu/Nd ratio) are used to infer the temporal variation of crustal thickness within this craton. Here, the zircon grains show depressed values of LREE/HREE ratio manifested in their higher Lu/Nd ratio which possibly attests the absence of thicker continental crust in Dharwar craton between 3.4-3.1 Ga. We would also try to establish our observation regarding the secular evolution of crustal thickness of Dharwar craton with the help of other bivariate plots using the other trace elemental proxies. Our result stand in contradiction with the finding of other workers who, with the help of geophysical parameters, inferred the greater thickness of continental crust attested in WDC within the said time frame  
Using offsets in airborne radar sounding and laser altimetry to characterize near-surface firn properties over the Greenland ice sheet
Authors: Rutishauser Anja , Ahlstrøm Andreas P. , Fausto Robert S. , Karlsson Nanna B. , Vandecrux Baptiste , Scanlan Kirk M. , Picard Ghislain , Andersen Signe B.
Published: March 2022

In recent decades, the Greenland Ice Sheet (GrIS) has experienced a significant increase in surface melting and meltwater runoff, which is now the main contributor to GrIS mass loss. In areas covered by firn, meltwater percolation and refreezing processes can significantly buffer meltwater runoff to the ocean. However, this process leads to the formation of ice layers and an overall firn densification, which is predicted to limit the firns’ meltwater storage capacity in the future. Additionally, the high spatial and temporal variability of ice layer formation and subsequent firn densification can cause large uncertainties in altimetry-derived mass balance estimates. Thus, understanding the spatial and vertical extent of ice layers in the firn is important to estimate the GrIS contribution to sea-level rise.

Due to limited direct observations of firn properties, modeling future meltwater runoff and processes over the rapidly changing GrIS firn facies remains challenging. Here, we present a prospective new technique that leverages concurrent airborne radar sounding and laser altimetry measurements to characterize near-surface firn over spatially extensive areas. We hypothesize that due to their different depth sensitivities, the presence of ice layers in the firn yields an offset between radar sounding- and laser-derived surface elevations (differential altimetry). We compare existing airborne radar and laser measurements to in-situ firn observations and use one-dimensional radar sounding simulations to investigate 1) the sensitivity of the differential altimetry technique to different firn facies, and 2) the techniques’ capability to estimate firn density and firn ice content. Preliminary results over the western GrIS show good correlations between differential altimetry signatures and areas of firn affected by percolation and refreezing processes.

Through this technique, we explore the potential to leverage a wealth of radar sounding measurements conducted at low frequencies (< 200 MHz), that typically do not resolve the firn structure, to derive near-surface firn properties. Finally, we apply the differential altimetry technique to data collected as part of NASA’s Operation IceBridge between 2009-2019 to derive spatio-temporal changes in the GrIS firn in response to climatic conditions, in particular the formation of ice layers and changes in firn ice content. Our results can help reduce uncertainties in satellite-derived mass balance measurements and improve firn models, which both contribute to reducing uncertainties in current and projected GrIS contributions to global sea-level rise.
Evaluating statistical downscaling for daily maximum and minimum temperatures in Argentina
Authors: Balmaceda-Huarte Rocio , Bettolli Maria Laura
Published: March 2022

Empirical statistical downscaling (ESD) under the perfect prognosis approach was carried out to simulate daily maximum (Tx) and minimum temperatures (Tn) in the different climatic regions of Argentina. In this regard, three ESD techniques: analogs (AN), generalized linear models (GLM) and neural networks (NN) were evaluated considering multiple predictor sets with a variety of configurations driven by three different reanalysis. ESD models were cross-validated with folds of non-consecutive years (1979-2014) and then evaluated in a warmer set of years ( 2015-2018). The focus of the assessment of the ESD models was put on some marginal and temporal aspects of Tx and Tn. Depending on the aspect analyzed, AN ,GLM or NN models were more/less skillful but no method fulfilled all the features of both predicand variables. In this sense, the predictor set and model configuration were key factors. The different predictor structures (point-wise, spatial-wise and combinations of them) introduced the main differences for each ESD method, regardless of the predictand variable, region and reanalysis choice. In addition, the differences observed in ESD models due to the reanalysis choice were notably lower than the ones obtained due to changes in the statistical family and model structure. In the case of predictor variables, no improvements were observed in Tx and Tn simulations when a more complex predictor set was considered. In the case of Tn, models’ skills considerably increased when humidity information was included in the predictor set.  Our results showed that downscaling models were able to capture the general characteristics of Tx and Tn in all regions, with better performance in the latter variable. Overall, promising results were obtained in the evaluation of the ESD models in Argentina which encourage us to continue exploring their potential in different applications. 
Adaptation of a snow cover scheme for complex topography areas: regional calibration over High Mountain Asia and application in global models
Authors: Lalande Mickaël , Ménégoz Martin , Krinner Gerhard , Ottlé Catherine
Published: March 2022

Snow cover strongly modulates the energy fluxes between the atmosphere and the Earth's surface. Indeed, snow has generally a much higher albedo compared to other surfaces and therefore reduces the amount of solar radiation absorbed by the surface. Moreover, because of its low conductivity, snow isolates the ground from the atmosphere, impacting soil surface temperatures and energy balance (Zhang 2005). In general circulation models (GCMs) the snow cover fraction (SCF) is usually a diagnostic variable derived from other snow quantities, as for instance, the snow water equivalent (SWE) or the snow depth (SD). The relationship between SWE and SCF varies from simple linear relationships to more advanced parameterizations taking into account the snow density allowing to represent the hysteresis effect between the accumulation phase and the more disparate melting phase (e.g., Niu and Yang 2007). Swenson and Lawrence (2012) highlighted strong differences of snow cover extents between plains and mountainous areas, which may be explained by the persistence of snow on the summits whereas a faster melting occurs in the valleys. However, the dependency of SCF on the topography is considered only in a reduced number of GCMs, whereas mountainous areas represent nearly 1/5 of the world's surface area (Huddlestone et al., 2003). In this study, we designed three new snow parameterizations that include the impact of the sub-grid topography on the SCF in the ORCHIDEE land surface model (LSM) coupled to the LMDZ atmospheric model (part of the French GCM of IPSL). This model shows a strong cold bias and an excess of SCF over the High Mountains of Asia (HMA)  (Lalande et al., 2021). The new SCF parameterizations are based on the following existing ones: Swenson and Lawrence (2012; hereafter SL12), Roesch et al. (2001; hereafter R01), and a modified version of Niu and Yang (2007; hereafter NY07). These new parameterizations were calibrated over HMA using a high-resolution snow reanalysis (Liu et al., 2021), and compared to a deep learning model trained on the reanalysis dataset. The calibrated parameterizations SL12, R01, and the modified version of NY07 were then tested in coupled ORCHIDEE/LMDZ simulations. Preliminary results show improvements in simulated snow cover in HMA but slight deterioration in other areas. They suggest also that calibration should be extended to other snow-covered areas and should include other parameters such as the type of vegetation in particular.
 Total Solar Irradiance monitored by  DARA/JTSIM : first light observations
Authors: Montillet Jean-Philippe , Finsterle Wolfgang , Haberreiter Margit , Schmutz Werner , Pfiffner Daniel , Koller Silvio , Gyo Manfred , Fang Wei , Ye Xin , Yang Dongjun , Wu Duo
Published: March 2022

The Joint Total Solar Irradiance Monitor (JTSIM) onboard the Fengyun-3E spacecraft has been launched successfully the 4th of July 2021. It aims at measuring the Total Solar Irradiance (TSI) in orbit. The instruments on the Fengyun-3E/JTSIM include the Digital Absolute Radiometer (DARA) from the Physikalisch Meteorologisches Observatorium, Davos and World Radiation Center (PMOD/WRC) and the Solar Irradiance Absolute Radiometer (SIAR) from the Changchun Institute of Optics, Fine Mechanics and Physics Chinese Academy of Sciences (CIOMP/CAS). The JTSIM experiment will use the two different types of TSI radiometers to track the stability of TSI measurements, and to better understand instrumental degradation in space. We will present results from this new experiment at first light. We will compare the measurements from DARA and SIAR over the first few months and relate them to other active missions (SOHO/VIRGO/PMO6v, SORCE/TSIS). 
A Bayesian probabilistic approach to estimate the focal mechanism of micro-earthquakes occurring at the Irpinia fault system, southern Italy.
Authors: Tarantino Stefania , Emolo Antonio , Adinolfi Guido Maria , Festa Gaetano , Zollo Aldo
Published: March 2022

We developed a Bayesian technique to infer the double-couple, focal mechanism parameters (strike, dip and slip angles) of an earthquake source. The method uses 3 independent datasets: P-wave peak amplitude and polarity and S-to-P amplitude ratio wherever it is available.

The Bayesian technique works even in absence of one dataset and easily integrates any prior information about the region of study. The parameter space is explored thanks to an octree strategy. The method estimates the Posterior pdf, where the maximum likelihood parameter values (MAP model) both for the principal and auxiliary plane are chosen as the final fault mechanism solution. Furtherly, the uncertainties as the projections of the semi-axis of the 68% confidence ellipsoid centred on the MAP model are provided.

The joint use of the three datasets allows to determine a solution even in the case of a limited number of stations that have recorded the event, which is the case for example for small magnitude earthquakes (M<3).

We applied and tested the methodology to a microearthquake sequence (ML 0.4-3.0) occurred in the Irpinia region, South Italy, using an uninformative prior distribution for the parameters. In this area, the background seismicity occurs in a volume delimited by the faults activated during the 1980 Irpinia M 6.9 earthquake. This faults system is complex and composed of northwest–southeast striking normal faults along the Apennines chain. A network of 3-component accelerometers and velocimeters is currently monitoring the area (Irpinia Seismic NETwork).

We inferred the focal mechanism of the earthquakes of the sequence. Our results show fault mechanism solutions which are consistent with previous studies, well reflecting the regional stress field. The focus on micro-seismicity can reveal characteristics useful to highlight behaviours of larger scale seismicity.
The revised Quaternary climatostratigraphy of the Arctic Ocean: linkages with insolation and sea-level changes
Authors: Hillaire-Marcel Claude , de Vernal Anne , Crucifix Michel
Published: March 2022

The revised late Pleistocene chronostratigraphy of the Arctic Ocean based on the pre-2000 magnetostratigraphic interpretation and chronological information from the decay of U-series daughter isotopes in sediments leads to reassigning "warm" vs "cold" climatostratigraphic intervals to distinct interglacial, interstadial, or stadial stages and shows a realistic linkage with high latitude insolation parameters and the global sea-level history. "Warm" episodes then match intervals with summer season insolation and sea-level elevation peaking above those of the early Holocene. Whereas the whole summer season insolation governs heat fluxes towards the Arctic Ocean, in relation with the North Atlantic Water inflow, sea level plays a complementary role as it governs the submergence of the Arctic Ocean shelves and the development of “sea-ice factories”. Sea level also controls the flux of warm and low-salinity Pacific water through the shallow Bering Strait, thus the heat budget of the Western Arctic and the salinity budget of the whole Arctic Ocean. The combination of both parameters indicates that climate conditions during recent interglacials were of distinct amplitude and timing vs those at lower latitudes. From MIS 10 to MIS 1, five short "warm" intervals (MIS 1, 3, 5e, 7, 9) were characterized by sea-ice rafting deposition of smectite and detrital carbonate-rich sediments with 230Th-excesses along major drifting sea-ice routes TransPolar Drift; Beaufort Gyre). These layers alternate with coarser layers linked to sporadic and short-duration, Circum-Arctic glacier surges, deposited during stadials. In contradistinction, the MIS 14 to MIS 10 interval have experienced a thick ice-cover (perennial ice or ice shelf) during long periods, including MIS 11 and possibly MIS 13. These interglacials depict relatively a low summer season insolation in contrast with that of other interglacials. Another feature merging from this revision is the shortness of the intervals with seasonally open sea-ice conditions. Often recorded by a few cm-thick sedimentary layers, these intervals are in phase with the mean summer season insolation (not the June solstice peak) and may have lasted a few ka at most, based on the example of the Holocene. Feedbacks from the Arctic Ocean towards climate/ocean conditions at lower latitudes include i) the effect of its sea-ice on albedo and latitudinal pressure gradients, and ii) the impact of its freshwater export on the Atlantic Meridional Overturning Circulation (AMOC). Due to its specific response to insolation and sea-level changes, the Arctic Ocean may have thus triggered out of phase climate and AMOC fluctuations during interglacials at lower latitudes, but it has globally remained a sediment-starved glacial ocean throughout most of the Brunhes epoch.
Integrating Human Factors into the Colour Design of Human-Machine Interfaces for Spatial Habitat
Authors: Jiang Ao , Yao Xiang , Foing Bernard , Westland Stephen , Hemingray Caroline , Mu Shulei
Published: March 2022

With the rapid advances in manned spaceflight technology, astronauts will stay in spatial habitat for a long time in the future, and spatial missions will be more diversified, which will place higher requirements on human-machine spacecraft systems. As an important visual element for interacting with astronauts, human-machine interfaces not only affect the astronauts’ physical, psychological and cognitive activities, but also their work efficiency and even the safety of the space mission. This study system investigated publications, videos and pictures from NASA, ESA, China Space Center and Roscosmos. It was found that colour elements play an important role in the life and work of astronauts and profoundly affect the habitability level of the space environment. At the same time, it was found that human physiological parameters, cognitive and decision-making abilities, human psychological factors are the main abilities affected by colour elements. Through sketching as well as 3D modelling and rendering, the relevant cabin interfaces of the future spatial habitat's areas for work, hygiene were designed. This study provides some enlightenment for future research on the colour design of spacecraft environments or lunar or Mars habitat environments.
Urban Flood Mapping Uncertainty Justification using Variable Drop-off in RF and XGBoost Algorithms: A Case Study of Marib City, Yemen
Authors: Al-Aizari Ali , AlThuwaynee Omar , Al-Masnay Yousef , ullah Kashif , Park Hyuck-Jin , Al-Areeq Nabil , Zhao Chunli , Liu ⃰ Xingpeng
Published: March 2022

Urban flooding hazards are thought to be linked to climatic variability changes and population expansion, land use, elevation, slope, curvature, distance to river or canal, inadequate drainage, and more severe rainfall patterns. Therefore, urban flood modeling and uncertainty in the mapping of risk prediction are critical and should be considered. For this purpose, demystifying uncertainty in urban flood susceptibility mapping was modeled using a variable drop-off in random forest (RF) and XGBoost algorithms. This paper investigates the uncertainty that may arise in prediction mapping analysis when the predictive relevance level of the predictor factors varies significantly and particularly when the receiver operating characteristic fails to represent the resultant susceptibility map sensitivity with the influence of predictive factors.

We used Sentinel-1 images and Landsat satellite data to create a flood inventory map to map flooded areas. In total, 240 random flood and non-flood points were chosen, with 75% used for training and 25% for validation. The area under the curve, overall accuracy, confusion matrix, number of parameters, overfitting, and significant distribution were compared. For both models, drainage density was the most important, followed by the elevation, rainfall, normalized difference vegetation index, and road distance. By contrast, topographic wetness index, slope, aspect, and curvature were the least critical variables. After implementing the episode, stopping at nine variables, and testing the overfitting, the XGBoost model outperforms the RF model in the final map output. By contrast, the RF algorithm shows the ability of the RF model to overcome overfitting when assessing maps from a single factor. The study results could lead to a new research path considering the uncertainty related to different parts of the susceptibility modeling procedure and possible solutions to quantifying it.
Utilizing Hyperspectral Remote Sensing to Detect Concentration of Cyanobacteria in Freshwater Ecosystems
Authors: Pirro Jalissa , Thomas Christopher , Wallace Cameron , Alpert Zoe , Tuohy Madison , de Smet Timothy , Yokota Kiyoko , Jackson Patrick , Cleckner Lisa , Wigdahl-Perry Courtney , Young Kelly , Amejecor Kely , Scheer Austin
Published: March 2022

Harmful algal blooms (HABs) are a threat to freshwater quality, public health, and aquatic ecosystems. The economic losses suffered by the agricultural, fishing, and tourism industries as a result of HABs exceed billions of dollars worldwide annually, with cleanup costs from local and national governments reaching a similar price. Current manual field-based sampling methods followed by laboratory analysis to detect and monitor HABs in expensive, labor-intensive, and slow, delaying critical management decisions. Moreover, current detection methods have limited success documenting HABs in freshwater bodies and such attempts employ satellite-based multispectral remote sensing; however, satellite-based methods are limited by cost, low spatial and spectral resolution, and restricting temporal windows for on-demand revisits. Our study used relatively low-cost unpiloted aerial systems (UAS) and hyperspectral sensors to detect HABs with higher resolution while having the capacity to conduct near real-time detection. Additionally, our hyperspectral remote sensing can detect and differentiate between HABs that produce cyanobacteria and other chlorophyll-producing plants. We detected a spectral peak of 710 nm that is characteristic of cyanobacteria producing HABs. Principal components analysis (PCA) was useful to spatially highlight HABs over wide areas. By utilizing hyperspectral remote sensing with UAS, HABs can be monitored and detected more efficiently. This new state-of-the-art research methodology will allow for targeted assessment, monitoring, and design of HABs management plans that can be adapted for other impacted inland freshwater bodies. 
SWAT+ application to a small catchment for NSWRM assessment
Authors: Negm Amro , Gaini Paolo , Chiaradia Enrico Antonio , Gandolfi Claudio
Published: March 2022

The assessment of soil-water balance is associated with several challenges, such as the mitigation effects of droughts and flooding, particularly under climate change. Such alerting threat has pushed forward the efforts that the governments are doing to mitigate this risk. Aiming at contributing to better characterize the soil-water balance in small agricultural catchments, the European Union has launched a project of OPtimal strategies to retAIN and re-use water and nutrients across different soil-climatic regions in Europe (OPTAIN). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862756. In the framework of this project, the Soil Water Assessment Tool Plus (SWAT+) model was applied to the Cherio river basin, located near the city of Milan, Northern Italy, to develop novel strategies for natural/small water retention measures (NSWRM). The topography of the basin is complex, in which the northern part of the basin is a mountainous area, while the middle and lower part is mainly covered by urban, forest, and agricultural areas. The digital elevation model, land uses, soils, river network, and a long dataset of observed meteorological variables from 2002 to 2020 were prepared and elaborated to satisfy the model requirements. The application of the SWAT+ model was done by delineating the watershed, mapping land use and soil and their associated parameters, and creating the Hydrologic Response Units (HRUs) that identify hydrological homogenous areas inside the basin. As a result, the SWAT+ was used to simulate the hydrological processes and a sensitivity analysis was performed to identify the sensitive parameters affecting the simulated discharge based on the Sobol method. Model calibration was then performed using the observed discharges recorded at a flow gauge close to the basin outlet. The results show that differences between the simulated and observed discharges are very significant and appear to be related to the insufficient quality of precipitation inputs, rather than to model limitations or poor parameter calibration. This returns to the role of the uncertainty associated with the temporal and spatial measurement of the precipitation even in small catchments when the hydromorphological characteristics are complex. The findings of this research can be used to have a better understanding of hydrological fluxes variability across the basin and to assess the proper NSWRM to improve the qualitative and quantitative management of water resources in the Cherio river basin.
An efficient data assimilation method for high resolution models of ocean circulation
Authors: Shapiro Georgy I. , Gonzalez-Ondina Jose Maria
Published: March 2022

Mesoscale and sub-mesoscale features such as eddies, fronts, boundary currents and localised upwellings play an important role in ocean dynamics, particularly in the coastal / shelf seas which are characterised by the values of Rossby radius much smaller than in the open ocean. Fine resolution ocean modelling is a ubiquitous practice to resolve such features. Due to inevitable errors,ocean models tend to drift from reality. A process called data assimilation (DA) is a way of keeping a model ‘on the tracks’ by constantly correcting it with fresh observations (DARC, 2021). Due to their complexity, operational data assimilation systems, such as NEMOVAR, DART or PDAF remained a topic mostly reserved to experts (Carrassi et al, 2018). This study presents a simple and computationally efficient method for DA in a high-resolution 3D ocean model, which is nested into a coarse-resolution good quality data assimilating (parent) model. The method is particularly suitable for localised coastal models which are run by small modelling groups. The method named Data Assimilation with Stochastic-Deterministic Downscaling (SDDA) reduces bias and root mean square errors (RMSE) of the high-resolution model. The basic idea is to assimilate data from the parent model instead of actual observations. In this way, the high-resolution model is physically aware of observations via the parent model. The method allows to avoid a complex process of assimilating the same observations which were already assimilated into the parent model. The method consists of two stages: (1) downscaling the parent model output onto the child model grid using Stochastic-Deterministic Downscaling (Shapiro et al, 2021), and (2) applying a Kalman gain formula to each of the fine grid nodes. The method is illustrated in a synthetic 2D case where the true solution is known, and the high-resolution model forecast (before data assimilation) is simulated by adding various types of errors. The SDDA method reduces the child model bias to the same level as in the parent model and reduces the RMSE typically by a factor of 2 to 5.

References

DARC, 2021 available at https://research.reading.ac.uk/met-darc/aboutus/what-is-data-assimilation/ last access: 05 August 2021

Carrassi, A., Bocquety ,M., Bertino, L. and Evensen,G. 2018. Data assimilation in the geosciences: An overview of methods, issues, and perspectives. WIRES climate change, v9, No5.

Shapiro, G. I., Gonzalez-Ondina, J. M., and Belokopytov, V. N., 2021. High-resolution stochastic downscaling method for ocean forecasting models and its application to the Red Sea dynamics, Ocean Sci., 17, 891–907.
PROM: a method for estimating error covariance functions for variational data assimilation schemes in ocean models 
Authors: Gonzalez-Ondina Jose M. , Shapiro Georgy I.
Published: March 2022

Variational data assimilation methods are an invaluable tool for operational ocean models. These methods require the knowledge of the spatial covariances of the background errors (differences between the numerical model and the true values) and the observation errors (differences between true and measured values). In practice, the true covariances are never known exactly, so the error covariance matrices containing values of the covariance functions at specific locations must be estimated approximately. The Hollingsworth and Lönnberg method (H-L), based on the analysis of innovations (differences between observations and the model), is a widely used method to compute the covariance matrices. It requires to combine into bins the products of innovation data separated by similar distances, compute covariances in each bin and then to find a best fit covariance function. While being a helpful tool, the H-L method has its limitations. We have developed a new mathematical method for computing the background and observation error covariance functions and therefore the error covariance matrices. The method (PROM) uses functional analysis to project the innovation products into a subspace of parametrised functions and then to find the element of this subspace that closest approximate the data. This allows to overcome some shortcomings of the H-L method like the assumption of statistical isotropy, allowing to compute 2D and 3D covariance functions. It also eliminates intermediate steps used in the H-L method such as binning the innovations, and the computation of innovation covariances for each bin, before the best-fit curve can be found. We show that the new method works in situations where the standard H-L method experiences difficulties, especially when observations are scarce. It gives a better estimate than the H-L in a synthetic idealised case where the true covariance function is known. We also show that the new method allows to use the separable convolution mathematical algorithm leading to a dramatic increase of the computational speed, up to an order of magnitude.
Evaluation of the effectiveness of mitigation measures to reduce pesticide inputs into surface water bodies via surface runoff and erosion
Authors: Klein Michael , Reichenberger Stefan , O'Connor Isabel , Spycher Simon , Sittig Stephan , Multsch Sebastian , Thomas Kai , Flade Jens , Großmann Dietlinde
Published: March 2022

Surface runoff from agricultural fields is a major input pathway of pesticides into surface waters. The aim of this project was to i) analyze the effectiveness of various mitigation measures to reduce pesticide runoff and erosion inputs into surface waters, ii) assess the suitability of the measures found effective for use in the quantitative environmental exposure assessment for authorization of plant protection products (PPP), and iii) make recommendations how the potentially suitable measures could be applied in risk assessment of PPP in Germany. Following a literature analysis, 16 risk mitigation measures were presented to five experts for evaluation of effectiveness, cost-effectiveness, controllability, current distribution and dissemination potential. Measures finally selected for quantitative analysis belong to 3 groups: vegetative filter strips (VFS), soil conservation measures and micro-dams in row crops. Subsequently, the effectiveness of the recommended measures was evaluated based on experimental data using different statistical methods (e.g. CART, MLR, graphical methods).

The quantitative analysis confirmed the effectiveness of VFS and micro-dams. For soil conservation measures (especially mulch-till), the evaluated data showed highly variable results. This was partly caused by the heterogeneity of the experimental data, which also made it difficult to aggregate the results of different studies.

The following conclusions were drawn: Both VFS and micro-dams can be recommended for application in quantitative environmental exposure assessment for pesticides.  However, infiltration and sedimentation in VFS should be simulated with a mechanistic model such as VFSMOD. The effect of micro-dams can be modelled as a reduction of the runoff Curve Number (CN). The runoff modelling should be carried out with a model such as PRZM which adjusts the CN daily based on soil water content.
Multiannual Atmospheric Controls on Drought Stationarity: What the NAO can tell us about past behaviours and future climate change projections?
Authors: Rust William , Bloomfield John , Cuthbert Mark , Corstanje Ron , Holman Ian
Published: March 2022

Atmospheric variability in the North Atlantic region is known to modulate hydrometeorological variables across Europe. In this context, oscillatory systems, such as the NAO, may be used to indicate future water resource behaviours, such as hydrological droughts. Existing hydroclimate studies have identified a sensitivity of certain water resources to multiannual periodicities in systems such as the NAO and have highlighted that these long-term behaviours may be valuable to existing drought forecasting systems; for instance, by indicating multi-year periods of increased drought risk. However, the importance of multiannual NAO periodicities for driving water resource behaviour, and the feasibility of this relationship for indicating future droughts, has yet to be assessed in the context of known non-stationarities that are internal to the NAO and its influence on European meteorological processes. Here, we explore the role of NAO periodicities in defining water resource and drought behaviours over the past 90 years using a large dataset of 136 groundwater level records and 767 streamflow gauges in the UK. We identify significant relationships between the NAO and a calculated index of wide-spread water resource drought and find several abrupt shifts in drought frequency driven by non-stationarities in multiannual NAO behaviour. This includes a 7.5-year periodicity that has predominated water resource behaviour (and extremes) since the 1970s but has been weakening over recent years, suggesting a new shift in drought frequency may soon impact water resources. Furthermore, we show that the degree to which these periodicities have influenced recorded water resource anomalies is comparable to the projected effects of a worst-case climate change scenario. We discuss the potential origins for these modes of non-stationarity and their implications for existing water resource forecasting and projection systems, as well as the utility of these periodic behaviours as an indicator of future water resource drought in Europe.
Effective Hydraulic Properties of Stony Soils: Simulation, Laboratory Experiment and Modeling
Authors: Naseri Mahyar , Iden Sascha C. , Durner Wolfgang
Published: March 2022

Stony soils are soils with a considerable volume of rock fragments (RF). They influence the soil hydraulic properties (SHP), including the water retention curve (WRC) and hydraulic conductivity curve (HCC). However, because of the challenges in the measurement and modeling of SHP in stony soils, RF are normally neglected in hydrological and land surface modeling.

In the present study, we measured SHP of stony soils with volumetric RF contents up to 50 % (v/v) in the laboratory using the simplified evaporation method. Afterward, we applied Hydrus 2D/3D software to create virtual stony soils with impermeable RF up to 37.3 % (v/v) in three spatial dimensions, 3D. The evaporation and multistep unit gradient experiments were simulated for the virtual stony soils, and inverse modeling in 1D was applied to identify their effective SHP. The identified effective SHP by measurement and inverse modeling were used to evaluate the available scaling models of hydraulic conductivity, such as the simple scaling model based on only the volume of RF (Ravina and Magier, 1984), and the most recent model, GEM, proposed by Naseri et al. (2020).

From the lab experiments, we successfully identified SHP of these stony soils for pressure heads from near saturation to -1000 cm. We also found that scaling the WRC of the background soil based on the volume of rock fragments gave reasonable effective SHP for low RF content, but was not appropriate for the highly stony soils. A higher reduction in conductivity was visible compared to the predicted values by the model of Ravina and Magier. Furthermore, comparison of the evaluated scaling models displayed a better performance of the GEM model especially when volume of RF in soil was low.

 

References:

Naseri, M., Peters, A., Durner, W. and Iden, S.C., 2020. Effective hydraulic conductivity of stony soils: General effective medium theory. Advances in Water Resources, 146, p.103765. DOI: 103765doi.org/10.1016/j.advwatres.2020.103765.

Ravina, I. and Magier, J., 1984. Hydraulic conductivity and water retention of clay soils containing coarse fragments. Soil Science Society of America Journal, 48(4), pp.736-740. DOI: 10.2136/sssaj1984.03615995004800040008x.
Improving Spatiotemporal Fine Particulate Matter from a Data Assimilation Approach
Authors: Zhang Xuguo , Fung Jimmy , Lau Alexis , Zhang Shaoqing , Huang Wei (Wayne)
Published: March 2022

The spatiotemporal concentration of multiple pollutants is crucial information for pollution control strategies to safeguard public health. Despite considerable efforts, however, significant uncertainty remains. In this study, a three-dimensional variational model is coupled with a data assimilation system to analyze the spatiotemporal variation of PM2.5 for the whole of China. Monthly simulations of six sensitivity scenarios in different seasons, including different assimilation cycles, are carried out to assess the impact of the assimilation frequency on the PM2.5 simulations and the model simulation accuracy afforded by data assimilation. The results show that the coupled system provides more reliable initial fields to substantially improve the model performance for PM2.5, PM10, and O3. Higher assimilation frequency improves the simulation in all geographic areas. Two statistical indicators—the root mean square error and the correlation coefficient of PM2.5 mass concentrations in the analysis field—are improved by 12.19 µg/m3 (33%) and 0.21 (48%), respectively. Although the 24-hour assimilation cycle considerably improves the model, assimilation at a 6-hour cycle raises the performance for PM2.5 to the performance goal level. The analysis shows that assimilating at a 24-hour cycle diminishes over time, whereas the positive impact of the 6-hour cycle persists. One pivotal finding is that the assimilation of PM2.5 in the outermost domain results in a substantial improvement in PM2.5 prediction for the innermost domain, which is a potential alternative method to the existing domain-wide data fusion algorithm. The effect of assimilation varies among topographies, a finding that provides essential support for further model development.