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4 results on '"UNIVERSITY OF BOLOGNA ITA"'

1. Discharge Estimation in Ungauged Basins Through Variational Data Assimilation: The Potential of the SWOT Mission

Author

I. Gejadze, Alessio Domeneghetti, Renato Prata de Moraes Frasson, Hind Oubanas, Marc Andre Fernand Durand, Pierre-Olivier Malaterre, R. Wei, Gestion de l'Eau, Acteurs, Usages (UMR G-EAU), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut de Recherche pour le Développement (IRD), UNIVERSITY COLUMBUS OHIO USA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), UNIVERSITY OF BOLOGNA ITA, Oubanas, H., Gejadze, I., Malaterre, P.-O., Durand, M., Wei, R., Frasson, R.P.M., Domeneghetti, A., Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO)

Subjects
Estimation, 010504 meteorology & atmospheric sciences, 0208 environmental biotechnology, SACRAMENTO COURS D'EAU, 02 engineering and technology, SWOT, Inverse problem, 01 natural sciences, 6. Clean water, 020801 environmental engineering, remote sensing, Data assimilation, Remote sensing (archaeology), [SDE]Environmental Sciences, inverse problem, Environmental science, PO COURS D'EAU, uncertainty, data assimilation, hydraulic modeling, SWOT analysis, 0105 earth and related environmental sciences, Water Science and Technology, Remote sensing
Abstract

[Departement_IRSTEA]Eaux [TR1_IRSTEA]GEUSI [ADD1_IRSTEA]Gestion intégrée de la ressource et des infrastructures; International audience; Space-borne instruments can measure river water surface elevation, slope and width. Remote sensing of river discharge in ungauged basins is far more challenging, however. This work investigates the estimation of river discharge from simulated observations of the forthcoming Surface Water and Ocean Topography (SWOT) satellite mission using a variant of the classical variational data assimilation method "4D-Var". The variational assimilation scheme simultaneously estimates discharge, river bathymetry and bed roughness in the context of a 1.5D full Saint Venant hydraulic model. Algorithms and procedures are developed to apply the method to fully ungauged basins. The method was tested on the Po and Sacramento Rivers. The SWOT hydrology simulator was used to produce synthetic SWOT observations at each overpass time by simulating the interaction of SWOT radar measurements with the river water surface and nearby land surface topography at a scale of approximately 1 m, thus accounting for layover, thermal noise and other effects. SWOT data products were synthesized by vectorizing the simulated radar returns, leading to height and width estimates at 200 m increments along the river centerlines. The ingestion of simulated SWOT data generally led to local improvements on prior bathymetry and roughness estimates which allowed the prediction of river discharge at the overpass times with relative root-mean-squared errors of 12.1% and 11.2% for the Po and Sacramento rivers respectively. Nevertheless, equifinality issues that arise from the simultaneous estimation of bed elevation and roughness may prevent their use for different applications, other than discharge estimation through the presented framework.

Published
2018

2. Trade-offs between carbon stocks and biodiversity in European temperate forests

Author

Frédéric Gosselin, Sabina Burrascano, Yoan Paillet, Juri Nascimbene, Thomas Campagnaro, Péter Ódor, Walter Mattioli, Francesco Maria Sabatini, Philippe Janssen, Christophe Bouget, Tobias Kuemmerle, R. B. de Andrade, Tommaso Sitzia, HUMBOLDT UNIVERVITY BERLIN DEU, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Ecosystèmes forestiers (UR EFNO), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), MTA Centre for Ecological Research [Tihany], Hungarian Academy of Sciences (MTA), Laboratoire des EcoSystèmes et des Sociétés en Montagne (UR LESSEM), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), UNIVERSITY OF BOLOGNA ITA, Sabatini, Francesco Maria, de Andrade, Rafael Barreto, Paillet, Yoan, Ódor, Péter, Bouget, Christophe, Campagnaro, Thoma, Gosselin, Frédéric, Janssen, Philippe, Mattioli, Walter, Nascimbene, Juri, Sitzia, Tommaso, Kuemmerle, Tobia, and Burrascano, Sabina

Subjects
0106 biological sciences, biodiversity conservation, carbon storage, climate change mitigation, community thresholds, multi-objective forest planning, multi-taxonomic diversity, trade-off species, win-win species, Global and Planetary Change, Environmental Chemistry, Ecology, 2300, 010504 meteorology & atmospheric sciences, CLIMATE CHANGE MITIGATION, Biodiversity, MULTI‐TAXONOMIC DIVERSITY, Forests, 01 natural sciences, COMMUNITY THRESHOLDS, CHAMPIGNON, CARBON STORAGE, Taxonomic rank, Lichen, General Environmental Science, Temperate forest, win-win specie, Geography, Italy, [SDE]Environmental Sciences, France, Temperate rainforest, Climate Change, Climate change, WIN‐WIN SPECIES, 010603 evolutionary biology, MULTI‐OBJECTIVE FOREST PLANNING, Quantitative Biology - Populations and Evolution, 0105 earth and related environmental sciences, Hungary, trade-off specie, TRADE‐OFF SPECIES, community threshold, Populations and Evolution (q-bio.PE), 15. Life on land, Carbon, BIODIVERSITY CONSERVATION, Taxon, 13. Climate action, FOS: Biological sciences, Species richness, GROUPE TAXONOMIQUE, carbon, biodiversity, climate change, forests
Abstract

Policies to mitigate climate change and biodiversity loss often assume that protecting carbon-rich forests provides co-benefits in terms of biodiversity, due to the spatial congruence of carbon stocks and biodiversity at biogeographic scales. However, it remains unclear whether this holds at the scales relevant for management, with particularly large knowledge gaps for temperate forests and for taxa other than trees. We built a comprehensive dataset of Central European temperate forest structure and multi-taxonomic diversity (beetles, birds, bryophytes, fungi, lichens, and plants) across 352 plots. We used Boosted Regression Trees to assess the relationship between above-ground live carbon stocks and (a) taxon-specific richness, (b) a unified multidiversity index. We used Threshold Indicator Taxa ANalysis to explore individual species' responses to changing above-ground carbon stocks and to detect change-points in species composition along the carbon-stock gradient. Our results reveal an overall weak and highly variable relationship between richness and carbon stock at the stand scale, both for individual taxonomic groups and for multidiversity. Similarly, the proportion of win-win and trade-off species (i.e. species favored or disadvantaged by increasing carbon stock, respectively) varied substantially across taxa. Win-win species gradually replaced trade-off species with increasing carbon, without clear thresholds along the above-ground carbon gradient, suggesting that community-level surrogates (e.g. richness) might fail to detect critical changes in biodiversity. Collectively, our analyses highlight that leveraging co-benefits between carbon and biodiversity in temperate forest may require stand-scale management that prioritizes either biodiversity or carbon-in order to maximize co-benefits at broader scales. Importantly, this contrasts with tropical forests, where climate [...], Pre-review Version 2018, 07\23 + Supplementary information 43 Pages, 5 figures + 9 supplementary Figures

Published
2019

3. Changing climate shifts timing of European floods

Author

Bloschl, Gunter, Hall, Julia, Parajka, Juraj, Perdigão, Rui A. P., Merz, Bruno, Arheimer, Berit, Aronica, Giuseppe T., Bilibashi, Ardian, Bonacci, Ognjen, Borga, Marco, Äanjevac, Ivan, Castellarin, Attilio, Chirico, Giovanni B., Claps, Pierluigi, Fiala, Kã¡roly, Frolova, Natalia, Gorbachova, Liudmyla, Gol, Ali, Hannaford, Jamie, Harrigan, Shaun, Kireeva, Maria, Kiss, Andrea, Kjeldsen, Thomas R., Kohnovã¡, Silvia, Koskela, Jarkko J., Ledvinka, Ondrej, Macdonald, Neil, Mavrova Guirguinova, Maria, Mediero, Luis, Merz, Ralf, Molnar, Peter, Montanari, Alberto, Murphy, Conor, Osuch, Marzena, Ovcharuk, Valeryia, Radevski, Ivan, Rogger, Magdalena, Salinas, Josà L., Sauquet, Eric, Å&nbsp, raj, Mojca, Szolgay, Jan, Viglione, Alberto, Volpi, Elena, Wilson, Donna, Zaimi, Klodian, Åivkoviä, Nenad, Blã¶schl, Gã¼nter, Hall, Julia, Parajka, Juraj, Perdigã£o, Rui A. P., Merz, Bruno, Arheimer, Berit, Aronica, Giuseppe T., Bilibashi, Ardian, Bonacci, Ognjen, Borga, Marco, Ä anjevac, Ivan, Castellarin, Attilio, Chirico, GIOVANNI BATTISTA, Claps, Pierluigi, Fiala, Kã¡roly, Frolova, Natalia, Gorbachova, Liudmyla, Gã¼l, Ali, Hannaford, Jamie, Harrigan, Shaun, Kireeva, Maria, Kiss, Andrea, Kjeldsen, Thomas R., Kohnovã¡, Silvia, Koskela, Jarkko J., Ledvinka, Ondrej, Macdonald, Neil, Mavrova guirguinova, Maria, Mediero, Lui, Merz, Ralf, Molnar, Peter, Montanari, Alberto, Murphy, Conor, Osuch, Marzena, Ovcharuk, Valeryia, Radevski, Ivan, Rogger, Magdalena, Salinas, Josã© L., Sauquet, Eric, Å raj, Mojca, Szolgay, Jan, Viglione, Alberto, Volpi, Elena, Wilson, Donna, Zaimi, Klodian, Živkoviä , Nenad, INSTITUTE OF HYDRAULIC ENGINEERING AND WATER RESOURCES MANAGEMENT TECHNISCHE UNIVERSITAT WIEN AUT, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), HELMHOLTZ CENTRE POTSDAM DEU, DEPARTMENT OF ENGINEERING UNIVERSITY OF MESSINA ITA, CONTROL SYSTEMS ENGINEERING RENEWABLE ENERGY SYSTEMS AND TECHNOLOGY TIRANA ALB, FACULTY OF CIVIL ENGINEERING ARCHITECTURE AND GEODESY SPLIT UNIVERSITY HRV, DEPARTMENT OF LAND ENVIRONMENT AGRICULTURE AND FORESTRY UNIVERSITY OF PADOVA ITA, University of Zagreb, DEPARTMENT OF CIVIL CHEMICAL ENVIRONMENTAL AND MATERIALS ENGINEERING UNIVERSITY OF BOLOGNA ITA, DEPARTMENT OF AGRICULTURAL SCIENCES UNIVERSITY OF NAPLES FEDERICO II ITA, DEPARTMENT OF ENVIRONMENT LAND AND INFRASTRUCTURE ENGINEERING POLITECNICO DI TORINO ITA, LOWER TISZA DISTRICT WATER DIRECTORATE SZEGED HUN, DEPARTMENT OF LAND HYDROLOGY LOMONOSOV MOSCOW STATE UNIVERSITY RUS, DEPARTMENT OF HYDROLOGICAL RESEARCH UKRAINIAN HYDROMETEOROLOGICAL INSTITUTE KIEV UKR, DEPARTMENT OF CIVIL ENGINEERING DOKUZ EYLUL UNIVERSITY IZMIR TUR, CENTRE FOR ECOLOGY AND HYDROLOGY WALLINGFORD OXFORDSHIRE GBR, DEPARTMENT OF ARCHITECTURE AND CIVIL ENGINEERING UNIVERSITY OF BATH GBR, DEPARTMENT OF LAND AND WATER RESOURCES MANAGEMENT FACULTY OF CICIL ENGINEERING SLOVAK UNIVERSITY OF TECHNOLOGY BRATISLAVA SVK, FINNISH ENVIRONMENT INSTITUTE HELSINKI FIN, CZECH HYDROMETEOROLOGICAL INSTITUTE PRAGUE CZE, DEPARTMENT OF GEOGRAPHY AND PLANNING AND INSTITUTE OF RISK AND UNCERTAINTY UNIVERSITY OF LIVERPOOL GBR, UNIVERSITY OF ARCHITECTURE CIVIL ENGINEERING AND GEODESY SOFIA BGR, DEPARTMENT OF CIVIL ENGINEERING HYDRAULIC ENERGY AND ENVIRONMENT TECHNICAL UNIVERSITY OF MADRID ESP, DEPARTMENT OF CATCHMENT HYDROLOGY HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH DEU, INSTITUTE OF ENVIRONMENTAL ENGINEERING ETH ZURICH CHE, IRISH CLIMATE ANALYSIS AND RESEARCH UNITS DEPARTMENT OF GEOGRAPHY MAYNOOTH UNIVERSITY IRL, DEPARTMENT OF HYDROLOGY AND HYDRODYNAMICS INSTITUTE OF GEOPHYSICS POLISH ACADEMY OF SCIENCES WARSAW POL, HYDROMETEOROLOGICAL INSTITUTE ODESSA STATE ENVIRONMENTAL UNIVERSITY UKR, INSTITUTE OF GEOGRAPHY FACULTY OF NATURAL SCIENCES AND MATHEMATICS SS CYRIL AND METHODIUS UNIVERSITY SKOPJE MKD, Hydrologie-Hydraulique (UR HHLY), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), FACULTY OF CIVIL AND GEODETIC ENGINEERING UNIVERSITY OF LJUBLJANA SVN, DEPARTMENT OF ENGINEERING UNIVERSITY ROMA ITA, NORWEIGIAN WATER RESOURCES AND ENERGY DIRECTORATE OSLO NOR, INSTITUTE OF GEOSCIENCES ENERGY WATER AND ENVIRONMENT POLYTECHNIC UNIVERSITY OF TIRANA ALB, FACULTY OF GEOGRAPHY UNIVERSITY OF BELGRADE SRB, Blöschl, Günter, Perdigão, Rui A. P., Čanjevac, Ivan, Chirico, Giovanni B., Fiala, Károly, Gül, Ali, Kohnová, Silvia, Mavrova-Guirguinova, Maria, Salinas, José L., Šraj, Mojca, Živković, Nenad, Perdigão, Rui A P, Aronica, Giuseppe T, Chirico, Giovanni B, Kjeldsen, Thomas R, Koskela, Jarkko J, and Salinas, José L

Subjects
Mediterranean climate, EUROPE, snowmelt, 010504 meteorology & atmospheric sciences, Meteorology, 0208 environmental biotechnology, flood timing, climate change, Europe, seasonality shift, ta1171, Winter storm, climate signal, Climate change, 02 engineering and technology, 01 natural sciences, Meteorology and Climatology, Hydrology (agriculture), Flooding, Spring (hydrology), flood, Europe, climate, SDG 13 - Climate Action, 0105 earth and related environmental sciences, geography, geography.geographical_feature_category, Multidisciplinary, Flood myth, Medicine (all), Flooding (psychology), 020801 environmental engineering, climate change, 13. Climate action, Snowmelt, [SDE]Environmental Sciences, climate change, climate signal, flood, snowmelt, soil moisture, Environmental science, Physical geography, Hydrology, soil moisture
Abstract

International audience; A warming climate is expected to have an impact on the magnitude and timing of river floods; however, no consistent large-scale climate change signal in observed flood magnitudes has been identified so far. We analyzed the timing of river floods in Europe over the past five decades, using a pan-European database from 4262 observational hydrometric stations, and found clear patterns of change in flood timing. Warmer temperatures have led to earlier spring snowmelt floods throughout northeastern Europe; delayed winter storms associated with polar warming have led to later winter floods around the North Sea and some sectors of the Mediterranean coast; and earlier soil moisture maxima have led to earlier winter floods in western Europe. Our results highlight the existence of a clear climate signal in flood observations at the continental scale.

Published
2017

4. The distributed model intercomparison project. Phase 2: experiment design and summary results of the western basin experiments

Author

Laurent Coron, Brian Cosgrove, Gabriele Coccia, Seann Reed, Yu Zhang, Arthur Henkel, Ziya Zhang, Michael Smith, Eric A. Anderson, Cinzia Mazzetti, Ezio Todini, Ismael Orozco, Charles Perrin, Fekadu Moreda, Feng Ding, David Kitzmiller, Soroosh Sorooshian, Peter Fickenscher, Audrey Valéry, John C. Schaake, Kuolin Hsu, Enrique Ortiz Andres, Robert Hartman, Vazken Andréassian, Naoki Mizukami, Ali Behrangi, Bisher Imam, Victor Koren, Scott Staggs, Zhengtao Cui, Félix Francés, Behnaz Khakbaz, OFFICE OF HYDROLOGIC DEVELOPMENT NOAA/NATIONAL WEATHER SERVICE SILVER SPRING USA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), UNIVERSITY CORPORATION FOR ATMOSPHERIC RESEARCH BOULDER USA, WATER AND ECOSYSTEMS MANAGEMENT RESEARCH TRIANGLE INSTITUTE RESEARCH TRIANGLE PARK USA, RESEARCH APPLICATIONS LABORATORY NATIONAL CENTER FOR ATMOSPHERIC RESEARCH BOULDER USA, ADNET SYSTEMS INC BETHESDA USA, Hydrosystèmes et Bioprocédés (UR HBAN), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), URS CORPORATION ONTARIO USA, CENTER FOR HYDROMETEOROLOGY AND REMOTE SENSING CHRS DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING UNIVERSITY OF CALIFORNIA IRVINE USA, ENGINEERING UNIVERSITY OF CALIFORNIA IRVINE USA JET PROPULSION LABORATORY CALIFORNIA INSTITUTE OF TECHNOLOGY PASADENA USA, INTERNATIONAL HYDROLOGICAL PROGRAMME IHP DIVISION OF WATER SCIENCES UNESCO PARIS FRA, DEPARTMENT OF EARTH AND GEO-ENVIRONMENTAL SCIENCES UNIVERSITY OF BOLOGNA ITA, PROGEA SRL VIA SANTO STEFANO BOLOGNA ITA, and RESEARCH INSTITUTE OF WATER ENGINEERING AND ENVIRONMENT UNIVERSITAT POLITECNICA DE VALENCIA ESP

Subjects
INGENIERIA HIDRAULICA, 010504 meteorology & atmospheric sciences, Meteorology, Hydrologic model, Hydrological modelling, 0207 environmental engineering, 02 engineering and technology, 01 natural sciences, MODELE RUNOFF, Benchmark (surveying), Streamflow, Flash flood, Range (statistics), 020701 environmental engineering, 0105 earth and related environmental sciences, Water Science and Technology, Lumped model, Distributed element model, Elevation, Snow, 6. Clean water, Rainfall-runoff, LUMPED MODEL, 13. Climate action, Calibration, [SDE]Environmental Sciences, Environmental science, Simulation, Distributed model
Abstract

The Office of Hydrologic Development (OHD) of the U.S. National Oceanic and Atmospheric Administration's (NOAA) National Weather Service (NWS) conducted the two phases of the Distributed Model Intercomparison Project (DMIP) as cost-effective studies to guide the transition to spatially distributed hydrologic modeling for operational forecasting at NWS River Forecast Centers (RFCs). Phase 2 of the Distributed Model lntercomparison Project (DMIP 2) was formulated primarily as a mechanism to help guide the U.S. NWS as it expands its use of spatially distributed watershed models for operational river, flash flood, and water resources forecasting. The overall purpose of DMIP 2 was to test many distributed models forced by high quality operational data with a view towards meeting NWS operational forecasting needs. At the same time, DMIP 2 was formulated as an experiment that could be leveraged by the broader scientific community as a platform for the testing, evaluation, and improvement of distributed models. DMIP 2 contained experiments in two regions: in the DMIP 1 Oklahoma basins, and second, in two basins in the Sierra Nevada Mountains in the western USA. This paper presents the overview and results of the DMIP 2 experiments conducted for the two Sierra Nevada basins. Simulations from five independent groups from France, Italy, Spain and the USA were analyzed. Experiments included comparison of lumped and distributed model streamflow simulations generated with uncalibrated and calibrated parameters, and simulations of snow water equivalent (SWE) at interior locations. As in other phases of DMIP, the participant simulations were evaluated against observed hourly streamflow and SWE data and compared with simulations provided by the NWS operational lumped model. A wide range of statistical measures are used to evaluate model performance on a run-period and event basis. Differences between uncalibrated and calibrated model simulations are assessed. Results indicate that in the two study basins, no single model performed best,in all cases. In addition, no distributed model was able to consistently outperform the lumped Model benchmark. However, one or more distributed models were able to outperform the lumped model benchmark in many of the analyses. Several calibrated distributed models achieved higher correlation and lower bias than the calibrated lumped benchmark in the calibration, validation, and combined periods. Evaluating a number of specific precipitation-runoff events, one calibrated distributed model was able to perform at a level equal to or better than the calibrated lumped model benchmark in terms of event-averaged peak and runoff volume error. However, three distributed models were able to provide improved peak timing compared to the lumped benchmark. Taken together, calibrated distributed models provided specific improvements over the lumped benchmark in 24% of the model-basin pairs for peak flow, 12% of the model-basin pairs for event runoff volume, and 41% of the model-basin pairs for peak timing. Model calibration improved the performance statistics of nearly all models (lumped and distributed). Analysis of several precipitation/runoff events indicates that distributed models may more accurately model the dynamics of the rain/snow line (and resulting hydrologic conditions) compared to the lumped benchmark model. Analysis of SWE simulations shows that better results were achieved at higher elevation observation sites. Although the performance of distributed models was mixed compared to the lumped benchmark, all calibrated models performed well compared to results in the DMIP 2 Oklahoma basins in terms of run period correlation and %Bias, and event-averaged peak and runoff error. This finding is noteworthy considering that these Sierra Nevada basins have complications such as orographically-enhanced precipitation, snow accumulation and melt, rain on snow events, and highly variable topography. Looking at these findings and those from the previous DMIP experiments, it is clear that at this point in their evolution, distributed models have the potential to provide valuable information on specific flood events that could complement lumped model simulations. Published by Elsevier B.V.

Published
2013

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