rnrfa: an R package to retrieve, filter and visualize data from the UK National River Flow Archive

The UK National River Flow Archive (NRFA) stores several types of hydrological data and metadata: daily river flow and catchment rainfall time series, gauging station and catchment information. Data are served through the NRFA web services via experimental RESTful APIs. Obtaining NRFA data can be unwieldy due to complexities in handling HTTP GET requests and parsing responses in JSON and XML formats. The rnrfa package provides a set of functions to programmatically access, filter, and visualize NRFA data using simple R syntax. This paper describes the structure of the rnrfa package, including examples using the main functions gdf() and cmr() for flow and rainfall data, respectively. Visualization examples are also provided with a shiny web application and functions provided in the package. Although this package is regional specific, the general framework and structure could be applied to similar databases

Exploring data mining for hydrological modelling

Technological advances in computer science, namely cloud computing and data mining, are reshaping the way the world looks at data. Data are becoming the drivers of discoveries and strategic developments. In environmental sciences, for instance, big volumes of information are produced by monitoring networks, satellites and model simulations and are processed to uncover hidden patterns, correlations and trends to, ultimately, support policy and decision making. Hydrologists, in particular, use models to simulate river discharges and estimate the concentration of pollutants as well as the risk of floods and droughts. The very first step of any hydrological modelling exercise consists of selecting an appropriate model. However, the choice is often made by the modeller based on his/her expertise rather than on the model's suitability to reproduce the most important processes for the area under study. Since this approach defeats the ``scientific method'' for its lack of reproducibility and consistency across experts as well as locations, a shift towards a data-driven selection process is deemed necessary. This work presents the design, development and testing results of a completely novel data mining algorithm, called AMCA, able to automatically identify the most suitable model configurations for a given catchment, using minimum data requirements and an inventory of model structures. In the design phase a transdisciplinary approach was adopted, borrowing techniques from the fields of machine learning, signal processing and marketing. The algorithm was tested on the Severn at Plynlimon flume catchment, in the Plynlimon study area (Wales, UK). This area was selected because of its reliable measurements and the homogeneity of its soils and vegetation. The Framework for Understanding Structural Errors (FUSE) was used as sample model inventory, but the methodology can easily be adapted to others, including more sophisticated model structures. The model configuration problem, that the AMCA attempts to solve, can be categorised as ``fully unsupervised'' if there is no prior knowledge of interactions and relationships amongst observed data at a certain location and available model structures and parameters. Therefore, the first set of tests was run on a synthetic dataset to evaluate the algorithm's performance against known outcomes. Most of the component of the synthetic model structure were clearly identified by the AMCA, which allowed to proceed with further testing using observed data. Using real observations, the AMCA efficiently selected the most suitable model structures and, when coupled with association rule mining techniques, could also identify optimal parameter ranges. The performance of the ensemble suggested by the combination of AMCA and association rules was calibrated and validated against four widely used models (Topmodel, ARNOVIC, PRMS and Sacramento). The ensemble configuration always returned the best average efficiency, characterised by the narrowest spread and, therefore, lowest uncertainty. As final application, the full set of FUSE models was used to predict the effect of land use changes on catchment flows. The predictive uncertainty improved significantly when the prior distributions of model structures and parameters were conditioned using the AMCA approach. It was also noticed that such improvement is due to constrains applied to both model and parameter space, however the parameter space seems to contribute more. These results confirm that a considerable part of the uncertainty in prediction is due to the definition of the prior choice of the model configuration and that more objective ways to constrain the prior using formal data-driven techniques are needed. AMCA is, however, a procedure that can only be applied to gauged catchment. Future experiments could test whether AMCA configurations could be regionalised or transferred to ungauged catchments on the basis of catchment characteristics.Open Acces

Heatwaves, droughts, and fires: exploring compound and cascading dry hazards at the pan-European scale

Compound and cascading natural hazards usually cause more severe impacts than any of the single hazard events alone. Despite the significant impacts of compound hazards, many studies have only focused on single hazards. The aim of this paper is to investigate spatio-temporal patterns of compound and cascading hazards using historical data for dry hazards, namely heatwaves, droughts, and fires across Europe. We streamlined a simple methodology to explore the occurrence of such events on a daily basis. Droughts in soil moisture were analyzed using time series of a threshold-based index, obtained from the LISFLOOD hydrological model forced with observations. Heatwave and fire events were analyzed using the ERA5-based temperature and Fire Weather Index datasets. The data used in this study relates to the summer seasons from 1990 to 2018. Our results show that joint dry hazard occurrences were identified in west, central, and east Europe, and with a lower frequency in southern Europe and eastern Scandinavia. Drought plays a substantial role in the occurrence of the compound and cascading events of dry hazards, especially in southern Europe as it drives duration of cascading events. Moreover, drought is the most frequent hazard-precursor in cascading events, followed by compound drought-fire events. Changing the definition of a cascading dry hazard by increasing the number of days without a hazard from 1 to 21 within the event (inter-event criterion), lowers as expected, the maximum number of cascading events from 94 to 42, and extends the maximum average duration of cascading events from 38 to 86 days. We had to use proxy observed data to determine the three selected dry hazards because long time series of reported dry hazards do not exist. A complete and specific database with reported hazards is a prerequisite to obtain a more comprehensive insight into compound and cascading dry hazards

Web technologies for environmental big data

Recent evolutions in computing science and web technology provide the environmental community with continuously expanding resources for data collection and analysis that pose unprecedented challenges to the design of analysis methods, workflows, and interaction with data sets. In the light of the recent UK Research Council funded Environmental Virtual Observatory pilot project, this paper gives an overview of currently available implementations related to web-based technologies for processing large and heterogeneous datasets and discuss their relevance within the context of environmental data processing, simulation and prediction. We found that, the processing of the simple datasets used in the pilot proved to be relatively straightforward using a combination of R, RPy2, PyWPS and PostgreSQL. However, the use of NoSQL databases and more versatile frameworks such as OGC standard based implementations may provide a wider and more flexible set of features that particularly facilitate working with larger volumes and more heterogeneous data sources

Mapping combined wildfire and heat stress hazards to improve evidence-based decision making

Heat stress and forest fires are often considered highly correlated hazards as extreme temperatures play a key role in both occurrences. This commonality can influence how civil protection and local responders deploy resources on the ground and could lead to an underestimation of potential impacts, as people could be less resilient when exposed to multiple hazards. In this work, we provide a simple methodology to identify areas prone to concurrent hazards, exemplified with, but not limited to, heat stress and fire danger. We use the combined heat and forest fire event that affected Europe in June 2017 to demonstrate that the methodology can be used for analysing past events as well as making predictions, by using reanalysis and medium-range weather forecasts, respectively. We present new spatial layers that map the combined danger and make suggestions on how these could be used in the context of a Multi-Hazard Early Warning System. These products could be particularly valuable in disaster risk reduction and emergency response management, particularly for civil protection, humanitarian agencies and other first responders whose role is to identify priorities during pre-interventions and emergencies

Proteomics coupled with AhR-reporter gene bioassay for human and environmental safety assessment of sewage sludge and hydrochar

Today application of sewage sludge (SL) and hydrochar (HC) in agriculture is a common practice for soil conditioning and crop fertilization, however safety concerns for human and environmental health due to the presence of toxic compounds have recently been expressed. Our aim was to test the suitability of proteomics coupled with bioanalytical tools for unravelling mixture effects of these applications in human and environmental safety assessment. We conducted proteomic and bioinformatic analysis of cell cultures used in the DR-CALUX® bioassay to identify proteins differentially abundant after exposure to SL and the corresponding HC, rather than only using the Bioanalytical Toxicity Equivalents (BEQs) obtained by DR-CALUX®. DR-CALUX® cells exposed to SL or HC showed a differential pattern of protein abundance depending on the type of SL and HC extract. The modified proteins are involved in antioxidant pathways, unfolded protein response and DNA damage that have close correlations with the effects of dioxin on biological systems and with onset of cancer and neurological disorders. Other cell response evidence suggested enrichment of heavy metals in the extracts. The present combined approach represents an advance in the application of bioanalytical tools for safety assessment of complex mixtures such as SL and HC. It proved successful in screening proteins, the abundance of which is determined by SL and HC and by the biological activity of legacy toxic compounds, including organohalogens

The case of study of hazelnut shells biorefinery: Synthesis of active carbons from the hydrochar recovered downstream of levulinic acid production

Hazelnut processing industry generates significant waste streams, in particular cuticles and shells. Extractives are the main components of the cuticle fraction (~36 wt%), mainly including polyphenols and fatty acids, which can be advantageously employed in the pharmaceutical and cosmetic industry. Focusing on the shell fraction, this represents ~50 % of the total nut weight. Differently from cuticles, shells are rich in recalcitrant lignin (~38 wt%), in addition to cellulose and hemicellulose (each component accounting for ~23 wt%). Up to now, this waste, which is preponderantly produced in Italy and Turkey, is mostly underutilized, being limitedly used as a boiler fuel for domestic heating and for landscaping. On the other hand, both these fractions of hazelnut shells can be successfully valorized and, in agreement with the objectives of the project PRIN 2020 LEVANTE “LEvulinic acid Valorization through Advanced Novel Technologies” (2020CZCJN7), we have proposed a new cascade approach, converting its cellulosic fraction into levulinic acid (∼9-12 wt%), recovering as final waste an abundant carbonaceous hydrochar (∼45 wt%), mainly composed of aromatic (from lignin) and furanic (from degradation of C5/C6 sugars) units. In the LEVANTE project, this hydrochar was activated by pyrolysis and chemical treatments (H3PO4, ZnCl2, KOH, NaOH), and the synthesized new active carbons (ACs) have been properly characterized (ultimate and proximate analysis, FT-IR, surface properties and SEM microscopy). This preliminary screening allowed us to select the KOH-AC as the most interesting one, as further confirmed by the highest CO2 adsorption capacity (~90 mg/g), due to its well-developed microporous texture. This new AC was also effective for the removal of the bulkier methylene blue (complete removal, corresponding to ~250 mg/g). This proposed integrated approach makes possible to fully exploit the hazelnut shell feedstock, smartly closing the biorefinery cycle of the hazelnut wastes, in a circular economy perspective. In addition, the selective fractionation of soluble C5 and C6 sugars of shell fraction is currently under investigation and this will enable us to obtain an hydrochar with a less-degraded lignin fraction, thus moving towards progressively more sustainable hydrothermal and activation reaction conditions. The authors are grateful to Italian “Ministero dell'Istruzione dell'Università e della Ricerca” for the financial support provided through the stated PRIN 2020 LEVANTE project

New exploitation strategies of the by-products deriving from the hazelnut supply chain

Hazelnut processing industry generates significant waste streams, in particular cuticles and shells. Extractives are the main components of the cuticle fraction (~36 wt%), mainly including polyphenols and fatty acids, which can be advantageously used in the pharmaceutical and cosmetic industry. Focusing on the shell fraction, this represents ~50 % of the total nut weight (about 273 thousand metric tons, based on the 2021-2022 worldwide data on hazelnut production). Differently from cuticles, shells are rich in recalcitrant lignin (~38 wt%), in addition to cellulose and hemicellulose (each component accounting for ~23 wt%). Up to now, this waste, which is preponderantly produced in Italy and Turkey, is mostly underutilized, being limitedly used as a boiler fuel for domestic heating and for landscaping. On the other hand, these both fractions of hazelnut shells can be successfully valorized and, in this perspective, we have proposed a new cascade approach, converting its cellulosic fraction into levulinic acid (∼9-12 wt%), and recovering an abundant carbonaceous hydrochar as the final waste (∼45 wt%), mainly composed of aromatic and furanic units. In this work, the exploitation of this waste biomass-derived hydrochar for environmental applications has been investigated, after its pyrolysis and chemical activation treatments (H3PO4, ZnCl2, KOH, NaOH). The synthesized new active carbons (ACs) have been properly characterized and used as adsorbents for CO2 and methylene blue removal. This proposed integrated approach makes possible to fully exploit the hazelnut shell feedstock, smartly closing the biorefinery cycle of the hazelnut wastes, in a sustainable and circular perspective