Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID â–² Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-1651 PEER REVIEWED ARTICLE 2015 Ana C. Brito , Carolina Sá , Carlos R. Mendes , Tim Brand , Ana M. Dias , Vanda Brotas , Keith Davidson Structure of late summer phytoplankton community in the Firth of Lorn (Scotland) using microscopy and HPLC-CHEMTAX 10.1016/j.ecss.2015.07.006 Simulation & Modeling Uncategorized No abstract available 607325
publications-1652 PEER REVIEWED ARTICLE 2016 Sonia Cristina , Davide DAlimonte , Priscila Costa Goela , Tamito Kajiyama , John Icely , Gerald Moore , Bruno Fragoso , Alice Newton Standard and Regional Bio-Optical Algorithms for Chlorophyll <inline-formula> <tex-math notation=xLaTeXx>$a$</tex-math> </inline-formula> Estimates in the Atlantic off the Southwestern Iberian Peninsula 10.1109/lgrs.2016.2529182 Simulation & Modeling Uncategorized No abstract available 607325
publications-1653 PEER REVIEWED ARTICLE 2014 P.C. Goela , S. Danchenko , J.D. Icely , L.M. Lubian , S. Cristina , A. Newton Using CHEMTAX to evaluate seasonal and interannual dynamics of the phytoplankton community off the South-west coast of Portugal 10.1016/j.ecss.2014.10.001 Simulation & Modeling Uncategorized No abstract available 607325
publications-1654 PEER REVIEWED ARTICLE 2015 Priscila C. Goela , John Icely , Sónia Cristina , Sergei Danchenko , T. Angel DelValls , Alice Newton Using bio-optical parameters as a tool for detecting changes in the phytoplankton community (SW Portugal) 10.1016/j.ecss.2015.07.037 Simulation & Modeling Uncategorized No abstract available 607325
publications-1655 PEER REVIEWED ARTICLE 2014 Philipp M.M. Groetsch , Stefan G.H. Simis , Marieke A. Eleveld , Steef W.M. Peters Cyanobacterial bloom detection based on coherence between ferrybox observations 10.1016/j.jmarsys.2014.05.015 Simulation & Modeling Uncategorized No abstract available 607325
publications-1656 PEER REVIEWED ARTICLE 2016 Philipp M. M. Groetsch , Stefan G. H. Simis , Marieke A. Eleveld , Steef W. M. Peters Spring blooms in the Baltic Sea have weakened but lengthened from 2000 to 2014 10.5194/bg-13-4959-2016 Simulation & Modeling Uncategorized Abstract. Phytoplankton spring bloom phenology was derived from a 15-year time series (2000–2014) of ship-of-opportunity chlorophyll a fluorescence observations collected in the Baltic Sea through the Alg@line network. Decadal trends were analysed against inter-annual variability in bloom timing and intensity, and environmental drivers (nutrient concentration, temperature, radiation level, wind speed).Spring blooms developed from the south to the north, with the first blooms peaking mid-March in the Bay of Mecklenburg and the latest bloom peaks occurring mid-April in the Gulf of Finland. Bloom duration was similar between sea areas (43 ± 2 day), except for shorter bloom duration in the Bay of Mecklenburg (36 ± 11 day). Variability in bloom timing increased towards the south. Bloom peak chlorophyll a concentrations were highest (and most variable) in the Gulf of Finland (20.2 ± 5.7 mg m−3) and the Bay of Mecklenburg (12.3 ± 5.2 mg m−3).Bloom peak chlorophyll a concentration showed a negative trend of −0.31 ± 0.10 mg m−3 yr−1. Trend-agnostic distribution-based (Weibull-type) bloom metrics showed a positive trend in bloom duration of 1.04 ± 0.20 day yr−1, which was not found with any of the threshold-based metrics. The Weibull bloom metric results were considered representative in the presence of bloom intensity trends.Bloom intensity was mainly determined by winter nutrient concentration, while bloom timing and duration co-varied with meteorological conditions. Longer blooms corresponded to higher water temperature, more intense solar radiation, and lower wind speed. It is concluded that nutrient reduction efforts led to decreasing bloom intensity, while changes in Baltic Sea environmental conditions associated with global change corresponded to a lengthening spring bloom period. 607325
publications-1657 PEER REVIEWED ARTICLE 2014 Sónia Cláudia Vitorino Cristina and Gerald Francis Moore and Priscila Raquel Fernandes Costa Goela and John David Icely and Alice Newton In situ validation of MERIS marine reflectance off the southwest Iberian Peninsula: assessment of vicarious adjustment and corrections for near-land adjacency 10.1080/01431161.2014.894657 Simulation & Modeling Uncategorized No abstract available 607325
publications-1658 PEER REVIEWED ARTICLE 2016 Priscila Costa Goela , Sónia Cristina , Tamito Kajiyama , John Icely , Gerald Moore , Bruno Fragoso , Alice Newton Technical Note: Algal Pigment Index 2 in the Atlantic off the Southwest Iberian Peninsula: standard and regional algorithms 10.5194/os-2016-41 Simulation & Modeling Uncategorized Abstract. In this study, Algal Pigment Index 2 (API2) is investigated in Sagres, an area located in the Atlantic off the southwestern Iberian Peninsula. Standard results provided by MEdium Resolution Image Spectrometer (MERIS) ocean color sensor were compared with alternative data products, determined through a regional inversion scheme, using both MERIS and in situ remote sensing reflectances (Rrs) as input data. The reference quantity for performance assessment is in situ total chlorophyll a (TChla) concentration estimated through phytoplankton absorption coefficient (i.e., equivalent to API2). Additional comparison of data products has also been addressed to TChla concentration determined by High Performance Liquid Chromatography. The MERIS matchup analysis revealed a systematic underestimation of TChla, which was confirmed with an independent comparison of product maps analysis. The study demonstrates the importance of regional algorithms for the study area that could complement upcoming standard results of the present Sentinel-3/OLCI space mission. 607325
publications-1659 PEER REVIEWED ARTICLE 2013 Mounce, S. R., Mounce, R. B., Jackson, T., Austin, J. and Boxall, J. B. Pattern matching and associative artificial neural networks for water distribution system time series data analysis 10.2166/hydro.2013.057 Simulation & Modeling Uncategorized Water distribution systems, and other infrastructures, are increasingly being pervaded by sensing technologies, collecting a growing volume of data aimed at supporting operational and investment decisions. These sensors monitor system characteristics, i.e. flows, pressures and water quality, such as in pipes. This paper presents the application of pattern matching techniques and binary associative neural networks for novelty detection in such data. A protocol for applying pattern matching to automatically recognise specific waveforms in time series based on their shapes is described together with a system called Advanced Uncertain Reasoning Architecture (AURA) Alert for autonomous determination of novelty. AURA is a class of binary neural network that has a number of advantages over standard artificial neural network techniques for condition monitoring including a sound theoretical basis to determine the bounds of the system operation. Results from application to several case studies are provided including both hydraulic and water quality data. In the case of pattern matching, the results demonstrated some transferability of burst patterns across District Metered Areas; however limitations in performance and difficulties with assembling pattern libraries were found. Results for the AURA system demonstrate the potential for robust event detection across multiple parameters providing valuable information for diagnosis; one example also demonstrates the potential for detection of precursor information, vital for proactive management. 619024
publications-1660 PEER REVIEWED ARTICLE 2017 S. R. Mounce , K. Ellis , J. M. Edwards , V. L. Speight , N. Jakomis , J. B. Boxall Ensemble Decision Tree Models Using RUSBoost for Estimating Risk of Iron Failure in Drinking Water Distribution Systems 10.1007/s11269-017-1595-8 Simulation & Modeling Uncategorized No abstract available 619024