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  • 1.
    Alfonso, Leonardo
    et al.
    IHE-Delft.
    Ridolfi, Elena
    Sapienza University of Rome, Rome, Italy.
    Gaytan-Aguilar, Sandra
    Deltares, Rotterdamseweg 185, Delft 2629 HD, Netherlands.
    Napolitano, Francesco
    Sapienza University of Rome.
    Russo, Fabio
    Sapienza University of Rome.
    Ensemble entropy for monitoring network design2014Inngår i: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 16, nr 3, s. 1365-1375Artikkel i tidsskrift (Fagfellevurdert)
  • 2.
    Biscarini, Chiara
    et al.
    University for Foreigners of Perugia, Perugia, Italy.
    Di Francesco, Silvia
    Niccolò Cusano University, Rome, Italy.
    Ridolfi, Elena
    University of Perugia, Perugia, Italy.
    Manciola, Piergiorgio
    University of Perugia, Perugia, Italy.
    On the simulation of floods in a narrow bending valley: The malpasset dam break case study2016Inngår i: Water, Vol. 8, nr 11, artikkel-id 545Artikkel i tidsskrift (Fagfellevurdert)
  • 3.
    Bloeschl, Gunter
    et al.
    Vienna Univ Technol, Inst Hydraul Engn & Water Resources Management, Vienna, Austria.
    Bierkens, Marc F. P.
    Univ Utrecht, Fac Geosci, Dept Phys Geog, Utrecht, Netherlands.
    Chambel, Antonio
    Univ Evora, Inst Earth Sci, Dept Geosci, Evora, Portugal.
    Cudennec, Christophe
    INRA, UMR SAS 1069, Agrocampus Ouest, Rennes, France.
    Destouni, Georgia
    Stockholm Univ, Dept Phys Geog, Stockholm, Sweden.
    Fiori, Aldo
    Roma Tre Univ, Dept Engn, Rome, Italy.
    Kirchner, James W.
    Swiss Fed Inst Technol, Dept Environm Syst Sci, Zurich, Switzerland;Swiss Fed Inst Forest Snow & Landscape Res WSL, Birmensdorf, Switzerland.
    McDonnell, Jeffrey J.
    Univ Saskatchewan, Global Inst Water Secur, Saskatoon, SK, Canada.
    Savenije, Hubert H. G.
    Delft Univ Technol, Dept Water Management, Delft, Netherlands.
    Sivapalan, Murugesu
    Univ Illinois, Dept Civil & Environm Engn, Urbana, IL USA;Univ Illinois, Dept Geog & Geog Informat Sci, Urbana, IL USA.
    Stumpp, Christine
    Univ Nat Resources & Life Sci, Inst Soil Phys & Rural Water Management, Vienna, Austria.
    Toth, Elena
    Univ Bologna, Dept Civil Chem Environm & Mat Engn DICAM, Bologna, Italy.
    Volpi, Elena
    Roma Tre Univ, Dept Engn, Rome, Italy.
    Carr, Gemma
    Vienna Univ Technol, Inst Hydraul Engn & Water Resources Management, Vienna, Austria.
    Lupton, Claire
    IAHS Ltd, CEH Wallingford, Wallingford, Oxon, England.
    Salinas, Jose
    Vienna Univ Technol, Inst Hydraul Engn & Water Resources Management, Vienna, Austria.
    Szeles, Borbala
    Vienna Univ Technol, Inst Hydraul Engn & Water Resources Management, Vienna, Austria.
    Viglione, Alberto
    Politecn Torino, Dept Environm Land & Infrastruct Engn DIATI, Turin, Italy.
    Aksoy, Hafzullah
    Istanbul Tech Univ, Dept Civil Engn, Istanbul, Turkey.
    Allen, Scott T.
    Swiss Fed Inst Technol, Dept Environm Syst Sci, Zurich, Switzerland.
    Amin, Anam
    Univ Padua, Dept Land Environm Agr & Forestry TESAF, Padua, Italy.
    Andreassian, Vazken
    Irstea, HYCAR Res Unit, Antony, France.
    Arheimer, Berit
    SMHI, Norrkoping, Sweden.
    Aryal, Santosh K.
    CSIRO Land & Water, Canberra, ACT, Australia.
    Baker, Victor
    Univ Arizona, Dept Hydrol & Atmospher Sci, Tucson, AZ USA.
    Bardsley, Earl
    Univ Waikato, Fac Sci & Engn, Hamilton, New Zealand.
    Barendrecht, Marlies H.
    Vienna Univ Technol, Inst Hydraul Engn & Water Resources Management, Vienna, Austria.
    Bartosova, Alena
    SMHI, Norrkoping, Sweden.
    Batelaan, Okke
    Flinders Univ S Australia, Coll Sci & Engn, NCGRT, Adelaide, SA, Australia.
    Berghuijs, Wouter R.
    Swiss Fed Inst Technol, Dept Environm Syst Sci, Zurich, Switzerland.
    Beven, Keith
    Univ Lancaster, Lancaster Environm Ctr, Lancaster, England.
    Blume, Theresa
    GFZ German Res Ctr Geosci, Hydrol Sect, Potsdam, Germany.
    Bogaard, Thom
    Delft Univ Technol, Dept Water Management, Delft, Netherlands.
    de Amorim, Pablo Borges
    Fed Univ Santa Catarina UFSC, Grad Program Environm Engn PPGEA, Florianopolis, SC, Brazil.
    Boettcher, Michael E.
    Leibniz Inst Baltic Sea Res IOW, Geochem & Isotope Biogeochem Grp, Warnemunde, Germany.
    Boulet, Gilles
    Univ Toulouse, CNES CNRS IRD INRA UPS, CESBIO, Toulouse, France.
    Breinl, Korbinian
    Vienna Univ Technol, Inst Hydraul Engn & Water Resources Management, Vienna, Austria.
    Brilly, Mitja
    Univ Ljubljana, Fac Civil Engn & Geodesy, Dept Environm Engn, Ljubljana, Slovenia.
    Brocca, Luca
    CNR, Res Inst Geohydrol Protect, Perugia, Italy.
    Buytaert, Wouter
    Imperial Coll London, Dept Civil & Environm Engn, London, England.
    Castellarin, Attilio
    Univ Bologna, Dept Civil Chem Environm & Mat Engn DICAM, Bologna, Italy.
    Castelletti, Andrea
    Politecn Milan, Dept Elect Informat & Bioengn, Milan, Italy.
    Chen, Xiaohong
    Sun Yat Sen Univ, Ctr Water Resources & Environm, Guangzhou, Guangdong, Peoples R China.
    Chen, Yangbo
    Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou, Guangdong, Peoples R China.
    Chen, Yuanfang
    Hohai Univ, Coll Hydrol & Water Resources, Nanjing, Jiangsu, Peoples R China.
    Chifflard, Peter
    Philipps Univ Marburg, Dept Geog, Marburg, Germany.
    Claps, Pierluigi
    Politecn Torino, Dept Environm Land & Infrastruct Engn DIATI, Turin, Italy.
    Clark, Martyn P.
    Univ Saskatchewan Canmore, Ctr Hydrol & Coldwater Lab, Canmore, AB, Canada.
    Collins, Adrian L.
    Rothamsted Res, Sustainable Agr Sci Dept, Okehampton, Devon, England.
    Croke, Barry
    Australian Natl Univ, Fenner Sch Environm & Soc, Canberra, ACT, Australia;Australian Natl Univ, Math Sci Inst, Canberra, ACT, Australia.
    Dathe, Annette
    Norwegian Inst Bioecon Res, Dept Water Resources, As, Norway.
    David, Paula C.
    Fed Univ Santa Catarina UFSC, Grad Program Environm Engn PPGEA, Florianopolis, SC, Brazil.
    de Barros, Felipe P. J.
    Univ Southern Calif, Sonny Astani Dept Civil & Environm Engn, Los Angeles, CA USA.
    de Rooij, Gerrit
    UFZ, Soil Syst Sci Dept, Helmholtz Ctr Environm Res, Halle, Saale, Germany.
    Di Baldassarre, Giuliano
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Geovetenskapliga sektionen, Institutionen för geovetenskaper, Luft-, vatten och landskapslära.
    Driscoll, Jessica M.
    US Geol Survey, Water Mission Area, Box 25046, Denver, CO 80225 USA.
    Duethmann, Doris
    Vienna Univ Technol, Inst Hydraul Engn & Water Resources Management, Vienna, Austria.
    Dwivedi, Ravindra
    Univ Arizona, Dept Hydrol & Atmospher Sci, Tucson, AZ USA.
    Eris, Ebru
    Ege Univ, Dept Civil Engn, Izmir, Turkey.
    Farmer, William H.
    US Geol Survey, Box 25046, Denver, CO 80225 USA.
    Feiccabrino, James
    Lund Univ, Dept Water Resources Engn, Lund, Sweden.
    Ferguson, Grant
    Univ Saskatchewan, Dept Civil Geol & Environm Engn, Saskatoon, SK, Canada.
    Ferrari, Ennio
    Univ Calabria, Dept Comp Engn Modeling Elect & Syst Sci Dimes, Arcavacata Di Rende, Italy.
    Ferraris, Stefano
    DIST Politecn, Turin, Italy;Univ Turin, Turin, Italy.
    Fersch, Benjamin
    Karlsruhe Inst Technol, Inst Meteorol & Climate Res, Garmisch Partenkirchen, Germany.
    Finger, David
    Reykjav Univ, Sch Sci & Engn, Reykjavik, Iceland;Reykjav Univ, SIF, Reykjavik, Iceland.
    Foglia, Laura
    Univ Calif Davis, Dept Land Air & Water Resources, Davis, CA 95616 USA.
    Fowler, Keirnan
    Univ Melbourne, Dept Infrastruct Engn, Parkville, Vic, Australia.
    Gartsman, Boris
    Russian Acad Sci IWP RAS, Water Problems Inst, Moscow, Russia.
    Gascoin, Simon
    Univ Toulouse, CNES CNRS IRD INRA UPS, CESBIO, Toulouse, France.
    Gaume, Eric
    IFSTTAR, Dept Geotech Environm Nat Hazards & Earth Sci, Nantes, France.
    Gelfan, Alexander
    Russian Acad Sci IWP RAS, Water Problems Inst, Moscow, Russia;Moscow MV Lomonosov State Univ, Fac Geog, Moscow, Russia.
    Geris, Josie
    Univ Aberdeen, Sch Geosci, Northern Rivers Inst, Aberdeen, Scotland.
    Gharari, Shervan
    Univ Saskatchewan, Sch Environm & Sustainabil, Saskatoon, SK, Canada.
    Gleeson, Tom
    Univ Victoria, Dept Civil Engn & Sch Earth & Ocean Sci, Victoria, BC, Canada.
    Glendell, Miriam
    James Hutton Inst, Environm & Biochem Sci Grp, Aberdeen, Scotland.
    Bevacqua, Alena Gonzalez
    Univ Fed Santa Catarina, Undergrad Programme Sanit & Environm Engn, Florianopolis, SC, Brazil.
    Gonzalez-Dugo, Maria P.
    Agr & Fisheries Res Inst Andalusia, IFAPA, Cordoba, Argentina.
    Grimaldi, Salvatore
    Tuscia Univ, Dept Innovat Biol Agrifood & Forest Syst DIBAF, Viterbo, Italy.
    Gupta, A. B.
    MNIT Jaipur, Dept Civil Engn, Jaipur, Rajasthan, India.
    Guse, Bjoern
    GFZ German Res Ctr Geosci, Hydrol Sect, Potsdam, Germany.
    Han, Dawei
    Univ Bristol, Dept Civil Engn, Bristol, Avon, England.
    Hannah, David
    Univ Birmingham, Sch Geog Earth & Environm Sci, Birmingham, W Midlands, England.
    Harpold, Adrian
    Univ Nevada, Nat Resources & Environm Sci Dept, Reno, NV 89557 USA.
    Haun, Stefan
    Univ Stuttgart, Inst Modelling Hydraul & Environm Syst, Stuttgart, Germany.
    Heal, Kate
    Univ Edinburgh, Sch GeoSci, Edinburgh, Midlothian, Scotland.
    Helfricht, Kay
    Austrian Acad Sci OAW, Inst Interdisciplinary Mt Res IGF, Innsbruck, Austria.
    Herrnegger, Mathew
    Univ Nat Resources & Life Sci, Inst Hydrol & Water Management, Vienna, Austria.
    Hipsey, Matthew
    Univ Western Australia, UWA Sch Agr & Environm, Perth, WA, Australia.
    Hlavacikova, Hana
    Slovak Hydrometeorol Inst, Dept Hydrol Forecasts & Warnings, Bratislava, Slovakia.
    Hohmann, Clara
    Karl Franzens Univ Graz, Wegener Ctr Climate & Global Change, Graz, Austria.
    Holko, Ladislav
    Slovak Acad Sci, Inst Hydrol, Bratislava, Slovakia.
    Hopkinson, Christopher
    Univ Lethbridge, Dept Geog, Lethbridge, AB, Canada.
    Hrachowitz, Markus
    Delft Univ Technol, Dept Water Management, Delft, Netherlands.
    Illangasekare, Tissa H.
    Colorado Sch Mines, Ctr Expt Study Subsurface Environm Proc, Golden, CO 80401 USA.
    Inam, Azhar
    McGill Univ, Dept Bioresource Engn, Montreal, PQ, Canada.
    Innocente, Camyla
    Fed Univ Santa Catarina UFSC, Grad Program Environm Engn PPGEA, Florianopolis, SC, Brazil.
    Istanbulluoglu, Erkan
    Univ Washington, Civil & Environm, Seattle, WA 98195 USA.
    Jarihani, Ben
    Univ Sunshine Coast, Sustainabil Res Ctr, Sippy Downs, Qld, Australia.
    Kalantari, Zahra
    Stockholm Univ, Dept Phys Geog, Stockholm, Sweden;Stockholm Univ, Bolin Ctr Climate Res, Stockholm, Sweden.
    Kalvans, Andis
    Univ Latvia, Fac Geog & Earth Sci, Riga, Latvia.
    Khanal, Sonu
    FutureWater, Wageningen, Netherlands.
    Khatami, Sina
    Univ Melbourne, Dept Infrastruct Engn, Parkville, Vic, Australia.
    Kiesel, Jens
    Leibniz Inst Freshwater Ecol & Inland Fisheries, Dept Ecosyst Res, Berlin, Germany.
    Kirkby, Mike
    Univ Leeds, Sch Geog, Leeds, W Yorkshire, England.
    Knoben, Wouter
    Univ Bristol, Dept Civil Engn, Bristol, Avon, England.
    Kochanek, Krzysztof
    Polish Acad Sci, Inst Geophys, Warsaw, Poland.
    Kohnova, Silvia
    Slovak Univ Technol Bratislava, Fac Civil Engn, Dept Land & Water Resources Management, Bratislava, Slovakia.
    Kolechkina, Alla
    Delft Univ Technol, Dept Water Management, Delft, Netherlands.
    Krause, Stefan
    Univ Birmingham, Sch Geog Earth & Environm Sci, Birmingham, W Midlands, England.
    Kreamer, David
    Univ Nevada, Dept Geosci, Las Vegas, NV 89154 USA.
    Kreibich, Heidi
    GFZ German Res Ctr Geosci, Hydrol Sect, Potsdam, Germany.
    Kunstmann, Harald
    Karlsruhe Inst Technol, Inst Meteorol & Climate Res, Garmisch Partenkirchen, Germany;Univ Augsburg, Inst Geog, Augsburg, Germany.
    Lange, Holger
    Norwegian Inst Bioecon Res, Dept Terr Ecol, As, Norway.
    Liberato, Margarida L. R.
    Univ Lisbon, Fac Ciencias, IDL, Vila Real, Portugal;Univ Tras Os Montes & Alto Douro UTAD, Vila Real, Portugal.
    Lindquist, Eric
    Boise State Univ, Sch Publ Serv, Boise, ID 83725 USA.
    Link, Timothy
    Univ Idaho, Water Resources Program, Moscow, ID USA.
    Liu, Junguo
    Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen, Peoples R China.
    Loucks, Daniel Peter
    Cornell Univ, Civil & Environm Engn, Ithaca, NY USA.
    Luce, Charles
    US Forest Serv, Boise, ID USA.
    Mahe, Gil
    Univ Montpellier, CNRS, HSM, IRD, Montpellier, France.
    Makarieva, Olga
    Melnikov Permafrost Inst, Yakutsk, Russia;St Petersburg State Univ, St Petersburg, Russia.
    Malard, Julien
    McGill Univ, Dept Bioresource Engn, Montreal, PQ, Canada.
    Mashtayeva, Shamshagul
    L Gumilev Eurasian Natl Univ, Dept Geog, Astana, Kazakhstan.
    Maskey, Shreedhar
    IHE Delft Inst Water Educ, Dept Water Sci & Engn, Delft, Netherlands.
    Mas-Pla, Josep
    Univ Girona, GAiA Geocamb, Girona, Spain;Catalan Inst Water Res, Girona, Spain.
    Mavrova-Guirguinova, Maria
    Univ Architecture Civil Engn & Geodesy, Sofia, Bulgaria.
    Mazzoleni, Maurizio
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Geovetenskapliga sektionen, Institutionen för geovetenskaper, Luft-, vatten och landskapslära.
    Mernild, Sebastian
    Nansen Environm & Remote Sensing Ctr, Bergen, Norway;Western Norway Univ Appl Sci, Dept Environm Sci, Sogndal, Norway;Univ Magallanes, Punta Arenas, Chile;Univ Bergen, Geophys Inst, Bergen, Norway.
    Misstear, Bruce Dudley
    Trinity Coll Dublin, Sch Engn, Dublin, Ireland.
    Montanari, Alberto
    Univ Bologna, Dept Civil Chem Environm & Mat Engn DICAM, Bologna, Italy.
    Mueller-Thomy, Hannes
    Vienna Univ Technol, Inst Hydraul Engn & Water Resources Management, Vienna, Austria.
    Nabizadeh, Alireza
    Shiraz Univ, Water Engn Dept, Shiraz, Iran.
    Nardi, Fernando
    Univ Foreigners Perugia, Water Resources Res & Documentat Ctr WARREDOC, Perugia, Italy.
    Neale, Christopher
    Univ Nebraska, Robert B Daugherty Water Food Global Inst, Lincoln, NE USA.
    Nesterova, Nataliia
    St Petersburg State Univ, State Hydrol Inst, St Petersburg, Russia.
    Nurtaev, Bakhram
    Odongo, Vincent
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Geovetenskapliga sektionen, Institutionen för geovetenskaper, Luft-, vatten och landskapslära.
    Panda, Subhabrata
    Bidhan Chandra Krishi Viswavidyalaya, Dept Soil & Water Conservat, Fac Agr, Haringhata, W Bengal, India.
    Pande, Saket
    Delft Univ Technol, Dept Water Management, Delft, Netherlands.
    Pang, Zhonghe
    Chinese Acad Sci, Inst Geol & Geophys, Beijing, Peoples R China.
    Papacharalampous, Georgia
    Natl Tech Univ Athens, Dept Water Resources & Environm Engn, Zografos, Greece.
    Perrin, Charles
    Irstea, HYCAR Res Unit, Antony, France.
    Pfister, Laurent
    Luxembourg Inst Sci & Technol, Dept Environm Res & Innovat, Catchment & Ecohydrol Res Grp, Belvaux, Luxembourg;Univ Luxembourg, Fac Sci Technol & Commun, Esch Sur Alzette, Luxembourg.
    Pimentel, Rafael
    Univ Cordoba, Andalusian Inst Earth Syst Res, Cordoba, Spain.
    Polo, Maria J.
    Univ Cordoba, Andalusian Inst Earth Syst Res, Cordoba, Spain.
    Post, David
    CSIRO Land & Water, Canberra, ACT, Australia.
    Sierra, Cristina Prieto
    Univ Cantabria, Environm Hydraul Inst IHCantabria, Santander, Spain.
    Ramos, Maria-Helena
    Irstea, HYCAR Res Unit, Antony, France.
    Renner, Maik
    Max Planck Inst Biogeochem Jena, Biospher Theory & Modelling Grp, Jena, Germany.
    Reynolds, Eduardo
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Geovetenskapliga sektionen, Institutionen för geovetenskaper, Luft-, vatten och landskapslära.
    Ridolfi, Elena
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Geovetenskapliga sektionen, Institutionen för geovetenskaper, Luft-, vatten och landskapslära.
    Rigon, Riccardo
    Univ Trento, DICAM, CUDAM, Trento, Italy.
    Riva, Monica
    Politecn Milan, Dept Civil & Environm Engn, Milan, Italy.
    Robertson, David E.
    CSIRO Land & Water, Clayton, Vic, Australia.
    Rosso, Renzo
    Politecn Milan, Dept Civil & Environm Engn, Milan, Italy.
    Roy, Tirthankar
    Princeton Univ, Dept Civil & Environm Engn, Princeton, NJ 08544 USA.
    Sa, Joao H. M.
    Fed Univ Santa Catarina UFSC, Grad Program Environm Engn PPGEA, Florianopolis, SC, Brazil.
    Salvadori, Gianfausto
    Univ Salento, Dept Math & Phys, Lecce, Italy.
    Sandells, Mel
    CORES Sci & Engn Ltd, Burnopfield, England.
    Schaefli, Bettina
    Univ Lausanne, Fac Geosci & Environm, Lausanne, Switzerland.
    Schumann, Andreas
    Ruhr Univ Bochum, Inst Hydrol Water Resources Management & Environm, Bochum, Germany.
    Scolobig, Anna
    Univ Geneva, Environm Governance & Terr Dev Inst, Geneva, Switzerland.
    Seibert, Jan
    Univ Zurich, Dept Geog, Zurich, Switzerland;Swedish Univ Agr Sci, Dept Aquat Sci & Assessment, Uppsala, Sweden.
    Servat, Eric
    Univ Montpellier, Montpellier, France.
    Shafiei, Mojtaba
    East Water & Environm Res Inst, Hydroinformat Dept, Mashhad, Razavi Khorasan, Iran.
    Sharma, Ashish
    Univ New South Wales, Civil & Environm Engn, Sydney, NSW, Australia.
    Sidibe, Moussa
    Coventry Univ, CAWR, Coventry, W Midlands, England.
    Sidle, Roy C.
    Univ Cent Asia, Mt Soc Res Inst, Khorog, Gbao, Tajikistan.
    Skaugen, Thomas
    Norwegian Water Resources & Energy Directorate, Oslo, Norway.
    Smith, Hugh
    Landcare Res, Palmerston North, New Zealand.
    Spiessl, Sabine M.
    Repository Safety Dept, Braunschweig, Germany.
    Stein, Lina
    Univ Bristol, Dept Civil Engn, Bristol, Avon, England.
    Steinsland, Ingelin
    NTNU Norwegian Univ Sci & Technol, Dept Math Sci, Trondheim, Norway.
    Strasser, Ulrich
    Univ Innsbruck, Dept Geog, Innsbruck, Austria.
    Su, Bob
    Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Enschede, Netherlands.
    Szolgay, Jan
    Slovak Univ Technol Bratislava, Fac Civil Engn, Dept Land & Water Resources Management, Bratislava, Slovakia.
    Tarboton, David
    Utah State Univ, Dept Civil & Environm Engn, Utah Water Res Lab, Logan, UT 84322 USA.
    Tauro, Flavia
    Tuscia Univ, Dept Innovat Biol Agrifood & Forest Syst DIBAF, Viterbo, Italy.
    Thirel, Guillaume
    Irstea, HYCAR Res Unit, Antony, France.
    Tian, Fuqiang
    Tsinghua Univ, Inst Hydrol & Water Resources, Beijing, Peoples R China.
    Tong, Rui
    Vienna Univ Technol, Inst Hydraul Engn & Water Resources Management, Vienna, Austria.
    Tussupova, Kamshat
    Lund Univ, Dept Water Resources Engn, Lund, Sweden.
    Tyralis, Hristos
    Hellen Air Force, Air Force Support Command, Elefsina, Greece.
    Uijlenhoet, Remko
    Wageningen Univ, Dept Environm Sci, Wageningen, Netherlands.
    van Beek, Rens
    Univ Utrecht, Fac Geosci, Dept Phys Geog, Utrecht, Netherlands.
    van der Ent, Ruud J.
    Univ Utrecht, Fac Geosci, Dept Phys Geog, Utrecht, Netherlands;Delft Univ Technol, Dept Water Management, Delft, Netherlands.
    van der Ploeg, Martine
    Wageningen Univ & Res, Soil Phys & Land Management Grp, Wageningen, Netherlands.
    Van Loon, Anne F.
    Univ Birmingham, Sch Geog Earth & Environm Sci, Birmingham, W Midlands, England.
    van Meerveld, Ilja
    Univ Zurich, Dept Geog, Zurich, Switzerland.
    van Nooijen, Ronald
    Delft Univ Technol, Dept Water Management, Delft, Netherlands.
    van Oel, Pieter R.
    Wageningen Univ, Dept Environm Sci, Wageningen, Netherlands.
    Vidal, Jean-Philippe
    Irstea, RiverLy Res Unit, Villeurbanne, France.
    von Freyberg, Jana
    Swiss Fed Inst Technol, Dept Environm Syst Sci, Zurich, Switzerland;Swiss Fed Inst Forest Snow & Landscape Res WSL, Birmensdorf, Switzerland.
    Vorogushyn, Sergiy
    GFZ German Res Ctr Geosci, Hydrol Sect, Potsdam, Germany.
    Wachniew, Przemyslaw
    AGH Univ Sci & Technol, Fac Phys & Appl Comp Sci, Krakow, Poland.
    Wade, Andrew J.
    Univ Reading, Dept Geog & Environm Sci, Reading, Berks, England.
    Ward, Philip
    Vrije Univ Amsterdam, Inst Environm Studies, Amsterdam, Netherlands.
    Westerberg, Ida K.
    IVL Swedish Environm Res Inst, Stockholm, Sweden.
    White, Christopher
    Univ Strathclyde, Dept Civil & Environm Engn, Glasgow, Lanark, Scotland.
    Wood, Eric F.
    Princeton Univ, Dept Civil & Environm Engn, Princeton, NJ 08544 USA.
    Woods, Ross
    Univ Bristol, Dept Civil Engn, Bristol, Avon, England.
    Xu, Zongxue
    Beijing Normal Univ, Coll Water Sci, Beijing, Peoples R China.
    Yilmaz, Koray K.
    Middle East Tech Univ, Dept Geol Engn, Ankara, Turkey.
    Zhang, Yongqiang
    Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface, Beijing, Peoples R China.
    Twenty-three unsolved problems in hydrology (UPH) - a community perspective2019Inngår i: Hydrological Sciences Journal, ISSN 0262-6667, E-ISSN 2150-3435, Vol. 64, nr 10, s. 1141-1158Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.

  • 4.
    Mineo, Claudio
    et al.
    Sapienza University of Rome.
    Ridolfi, Elena
    Dipartimento di Ingegneria Civile e Ambientale, Università di Perugia, DICA, Italy.
    Bertini, Claudia
    Sapienza University of Rome.
    Napolitano, Francesco
    Sapienza University of Rome.
    Kinetic energy and rainfall intensity relationships: A review2019Konferansepaper (Fagfellevurdert)
  • 5.
    Mineo, Claudio
    et al.
    Univ Roma, Dipartimento Ingn Civile Edile & Ambientale, I-00184 Rome, Italy.
    Ridolfi, Elena
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Geovetenskapliga sektionen, Institutionen för geovetenskaper, Luft-, vatten och landskapslära. Ctr Nat Hazards & Disaster Sci, CNDS, S-75236 Uppsala, Sweden.
    Moccia, Benedetta
    Univ Roma, Dipartimento Ingn Civile Edile & Ambientale, I-00184 Rome, Italy.
    Russo, Fabio
    Univ Roma, Dipartimento Ingn Civile Edile & Ambientale, I-00184 Rome, Italy.
    Napolitano, Francesco
    Univ Roma, Dipartimento Ingn Civile Edile & Ambientale, I-00184 Rome, Italy.
    Assessment of Rainfall Kinetic-Energy-Intensity Relationships2019Inngår i: Water, ISSN 2073-4441, E-ISSN 2073-4441, Vol. 11, nr 10, artikkel-id 1994Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Raindrop-impact-induced erosion starts when detachment of soil particles from the surface results from an expenditure of raindrop energy. Hence, rain kinetic energy is a widely used indicator of the potential ability of rain to detach soil. Although it is widely recognized that knowledge of rain kinetic energy plays a fundamental role in soil erosion studies, its direct evaluation is not straightforward. Commonly, this issue is overcome through indirect estimation using another widely measured hydrological variable, namely, rainfall intensity. However, it has been challenging to establish the best expression to relate kinetic energy to rainfall intensity. In this study, first, kinetic energy values were determined from measurements of an optical disdrometer. Measured kinetic energy values were then used to assess the applicability of the rainfall intensity relationship proposed for central Italy and those used in the major equations employed to estimate the mean annual soil loss, that is, the Universal Soil Loss Equation (USLE) and its two revised versions (RUSLE and RUSLE2). Then, a new theoretical relationship was developed and its performance was compared with equations found in the literature.

  • 6.
    Mineo, Claudio
    et al.
    Sapienza University of Rome.
    Ridolfi, Elena
    University of Perugia, Perugia, IT.
    Napolitano, Francesco
    Sapienza University of Rome.
    Russo, Fabio
    Sapienza University of Rome.
    The areal reduction factor: A new analytical expression for the Lazio Region in central Italy2018Inngår i: Journal of Hydrology, ISSN 0022-1694, E-ISSN 1879-2707, Vol. 560, s. 471-479Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    For the study and modeling of hydrological phenomena, both in urban and rural areas, a proper estimation of the areal reduction factor (ARF) is crucial. In this paper, we estimated the ARF from observed rainfall data as the ratio between the average rainfall occurring in a specific area and the point rainfall. Then, we compared the obtained ARF values with some of the most widespread empirical approaches in literature which are used when rainfall observations are not available. Results highlight that the literature formulations can lead to a substantial over- or underestimation of the ARF estimated from observed data. These findings can have severe consequences, especially in the design of hydraulic structures where empirical formulations are extensively applied. The aim of this paper is to present a new analytical relationship with an explicit dependence on the rainfall duration and area that can better represent the ARF-area trend over the area case of study. The analytical curve presented here can find an important application to estimate the ARF values for design purposes. The test study area is the Lazio Region (central Italy).

  • 7.
    Mineo, Claudio
    et al.
    Sapienza University of Rome.
    Ridolfi, Elena
    Dipartimento di Ingegneria Civile e Ambientale, Università di Perugia, DICA, Italy.
    Neri, Andrea
    Sapienza University of Rome.
    Russo, Fabio
    Sapienza University of Rome.
    Areal reduction factor: The effect of the return period2019Konferansepaper (Fagfellevurdert)
  • 8.
    Montesarchio, Valeria
    et al.
    Università Niccolò Cusano, Rome, Italy.
    Napolitano, Francesco
    Sapienza University of Rome.
    Rianna, Maura
    Sapienza University of Rome, Rome, Italy.
    Ridolfi, Elena
    Sapienza University of Rome, Rome, Italy.
    Russo, Fabio
    Sapienza University of Rome.
    Sebastianelli, Stefano
    Sapienza University of Rome.
    Comparison of methodologies for flood rainfall thresholds estimation2015Inngår i: Natural Hazards, ISSN 0921-030X, E-ISSN 1573-0840, Vol. 75, nr 1, s. 909-934Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A flood warning system based on rainfall thresholds makes it possible to issue alarms via an off-line approach. This technique is useful for mitigating the effects of flooding in small-to-medium-sized basins characterized by an extremely rapid response to rainfall. Rainfall threshold values specify the amount of precipitation that occurs over a given period of time and are dependent on both the amount of soil moisture and the spatiotemporal distribution of the rainfall. The precipitation generates a critical discharge in a particular river cross section. Exceeding these values can produce a critical situation in river sites that make them susceptible to flooding. In this work, we present a comparison of methodologies for estimating rainfall thresholds. Critical precipitation amounts are evaluated using empirical data, hydrological simulations and probabilistic methods. The study focuses on three small-to-medium-sized basins located in central Italy. For each catchment, historical data are first used to theoretically evaluate the empirical rainfall thresholds. Next, we calibrate a semi-distributed hydrological model that is validated using rain gauge and weather radar data. Critical rainfall depths over 30 min and 1, 3, 6, 12 and 24 h durations are then evaluated using the hydrological simulation. In the probabilistic approach, rainfall threshold values result from a minimization of two different functions, one following the Bayesian decision theory and the other following the informative entropy concept. In order to implement both functions, it is necessary to evaluate the joint probability function. The joint probability function is built up as a bivariate distribution of rainfall depth for a given duration with the corresponding flow peak value. Finally, in order to assess the performance of each methodology, we construct contingency tables to highlight the system performance.

  • 9.
    Montesarchio, Valeria
    et al.
    Sapienza University of Rome, Rome, Italy.
    Napolitano, Francesco
    Sapienza University of Rome.
    Ridolfi, Elena
    Sapienza University of Rome, Rome, Italy.
    Ubertini, Lucio
    Sapienza University of Rome, Rome, Italy.
    A comparison of two rainfall disaggregation models2012Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Whitin the context of flood management, and generally for performing environmental, climate, hydrological, and water resources analysis, it is useful and reliable to provide scenarios by rainfall simulation, in order to overcome data limitations in terms of time and spatial resolution. Generally, it is required that the stochastic model preservesimportant properties of the rainfall process, such as intermittency, seasonality and scaling behavior in space and time, so that there will be no substantial differences between historical rainfall data and synthetic records. In this work, two rainfall disaggregation models are evaluated in terms of their ability to reproduce rainfall hourly statistics in four sites in Central Italy. The considered models are an entropy based disaggregation model and Hyetos-R (Bartlett-Lewis rectangular pulses rainfall)

  • 10.
    Montesarchio, Valeria
    et al.
    Sapienza University of Rome, Rome, Italy.
    Ridolfi, Elena
    Sapienza University of Rome, Rome, Italy.
    Russo, Fabio
    Sapienza University of Rome.
    Napolitano, Francesco
    Sapienza University of Rome.
    Rainfall threshold definition using an entropy decision approach and radar data2012Inngår i: Natural hazards and earth system sciences, ISSN 1561-8633, E-ISSN 1684-9981, Vol. 7, s. 2061-2074Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Flash flood events are floods characterised by a very rapid response of basins to storms, often resulting in loss of life and property damage. Due to the specific space-time scale of this type of flood, the lead time available for triggering civil protection measures is typically short. Rainfall threshold values specify the amount of precipitation for a given duration that generates a critical discharge in a given river cross section. If the threshold values are exceeded, it can produce a critical situation in river sites exposed to alluvial risk. It is therefore possible to directly compare the observed or forecasted precipitation with critical reference values, without running online real-time forecasting systems. The focus of this study is the Mignone River basin, located in Central Italy. The critical rainfall threshold values are evaluated by minimising a utility function based on the informative entropy concept and by using a simulation approach based on radar data. The study concludes with a system performance analysis, in terms of correctly issued warnings, false alarms and missed alarms.

  • 11.
    Rianna, Maura
    et al.
    Sapienza University of Rome, Rome, Italy.
    Ridolfi, Elena
    University of Perugia, Perugia, Italy.
    Napolitano, Francesco
    Sapienza University of Rome.
    Comparison of different hydrological similarity measures to estimate flow quantiles2017Konferansepaper (Fagfellevurdert)
  • 12.
    Ridolfi, Elena
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Geovetenskapliga sektionen, Institutionen för geovetenskaper, Luft-, vatten och landskapslära. Centre of Natural Hazards and Disaster Science, CNDS, Uppsala, Sweden.
    Albrecht, Frederike
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Geovetenskapliga sektionen, Institutionen för geovetenskaper, Luft-, vatten och landskapslära. Department of Security, Strategy and Leadership, Swedish Defence University, Stockholm, Sweden.
    Di Baldassarre, Giuliano
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Geovetenskapliga sektionen, Institutionen för geovetenskaper, Luft-, vatten och landskapslära. Centre of Natural Hazards and Disaster Science, CNDS, Uppsala, Sweden.
    Exploring the role of risk perception in influencing flood losses over time2019Inngår i: Hydrological Sciences Journal, ISSN 0262-6667, E-ISSN 2150-3435, Vol. 65, nr 1, s. 12-20Artikkel i tidsskrift (Fagfellevurdert)
  • 13.
    Ridolfi, Elena
    et al.
    Sapienza Univ Roma, Dipartimento Ingn Civile Edile & Ambientale, Rome, Italy..
    Alfonso, L.
    UNESCO IHE, Hydroinformat Chair Grp, Delft, Netherlands..
    Di Baldassarre, Giuliano
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Geovetenskapliga sektionen, Institutionen för geovetenskaper, Luft-, vatten och landskapslära.
    Napolitano, F.
    Sapienza Univ Roma, Dipartimento Ingn Civile Edile & Ambientale, Rome, Italy..
    Optimal Cross-sectional Sampling for River Modelling with Bridges: an Information Theory-based Method2016Inngår i: Proceedings Of The International Conference On Numerical Analysis And Applied Mathematics 2015 (ICNAAM-2015), 2016, artikkel-id 430004Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The description of river topography has a crucial role in accurate one-dimensional (1D) hydraulic modelling. Specifically, cross-sectional data define the riverbed elevation, the flood-prone area, and thus, the hydraulic behavior of the river. Here, the problem of the optimal cross-sectional spacing is solved through an information theory-based concept. The optimal subset of locations is the one with the maximum information content and the minimum amount of redundancy. The original contribution is the introduction of a methodology to sample river cross sections in the presence of bridges. The approach is tested on the Grosseto River (IT) and is compared to existing guidelines. The results show that the information theory-based approach can support traditional methods to estimate rivers' cross-sectional spacing.

  • 14.
    Ridolfi, Elena
    et al.
    Sapienza University of Rome.
    Alfonso, Leonardo
    Di Baldassarre, Giuliano
    Dottori, Francesco
    Russo, F
    Napolitano, F
    An entropy approach for the optimization of cross-section spacing for river modelling2014Inngår i: Hydrological Sciences Journal, ISSN 0262-6667, E-ISSN 2150-3435, Vol. 59, nr 1, s. 126-137Artikkel i tidsskrift (Fagfellevurdert)
  • 15.
    Ridolfi, Elena
    et al.
    University of Perugia, Perugia, Italy.
    Buffi, Giulia
    University of Perugia, Perugia, Italy.
    Venturi, Sara
    University of Perugia, Perugia, Italy.
    Manciola, Piergiorgio
    University of Perugia, Perugia, Italy.
    Accuracy analysis of a dam model from drone surveys2017Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, nr 8, artikkel-id 1777Artikkel i tidsskrift (Fagfellevurdert)
  • 16.
    Ridolfi, Elena
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Geovetenskapliga sektionen, Institutionen för geovetenskaper, Luft-, vatten och landskapslära. CNDS, Ctr Nat Hazards & Disaster Sci, S-75236 Uppsala, Sweden.
    Di Francesco, Silvia
    Niccolo Cusano Univ, I-00166 Rome, Italy.
    Pandolfo, Claudia
    Ctr Funz Reg Umbria CFD, I-06034 Foligno, Italy.
    Berni, Nicola
    Ctr Funz Reg Umbria CFD, I-06034 Foligno, Italy.
    Biscarini, Chiara
    Univ Foreigners Perugia, UNESCO Chair Water Resources Management & Culture, I-06123 Perugia, Italy.
    Manciola, Piergiorgio
    Univ Perugia, Dept Civil & Environm Engn, I-06125 Perugia, Italy.
    Coping with Extreme Events: Effect of Different Reservoir Operation Strategies on Flood Inundation Maps2019Inngår i: Water, ISSN 2073-4441, E-ISSN 2073-4441, Vol. 11, nr 5, artikkel-id 982Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The need of addressing residual flood risk associated with structural protection measures, such as levee systems and flood-control reservoirs, has fostered actions aimed at increasing flood risk awareness. Structural measures have lowered risk perception by inducing a false sense of safety. As a result, these structures contribute to an underestimation of the residual risk. We analyze the effect of different reservoir operations, such as coping with drought versus coping with flood events, on flood inundation patterns. First, a hydrological model simulates different scenarios, which represent the dam regulation strategies. Each regulation strategy is the combination of an opening of the outlet gate and of the initial water level in the reservoir. Second, the corresponding outputs of the dam in terms of maximum discharge values are estimated. Then, in turn, each output of the dam is used as an upstream boundary condition of a hydraulic model used to simulate the flood propagation and the inundation processes in the river reach. The hydraulic model is thus used to determine the effect, in terms of inundated areas, of each dam regulation scenario. Finally, the ensemble of all flood inundation maps is built to define the areas more prone to be flooded. The test site is the Casanuova dam (Umbria, central Italy) which aims at: (i) mitigating floods occurring at the Chiascio River, one of the main tributaries of Tiber River, while (ii) providing water supply for irrigation. Because of these two competitive interests, the understanding of different scenarios generated by the dam operations offers an unique support to flood mitigation strategies. Results can lead to draw interesting remarks for a wide number of case studies.

  • 17.
    Ridolfi, Elena
    et al.
    Sapienza University of Rome.
    Grimaldi, Salvatore
    Tuscia University, Viterbo, Italy.
    Napolitano, Francesco
    Sapienza University of Rome.
    A bivariate analysis of temperature and rainfall series for snowfall return period estimation2015Konferansepaper (Fagfellevurdert)
  • 18.
    Ridolfi, Elena
    et al.
    University of Perugia, Perugia, Italy.
    Manciola, Piergiorgio
    University of Perugia, Perugia, Italy.
    Water level measurements from drones: A Pilot case study at a dam site2018Inngår i: Water, ISSN 2073-4441, Vol. 10, nr 3, artikkel-id 297Artikkel i tidsskrift (Fagfellevurdert)
  • 19.
    Ridolfi, Elena
    et al.
    Sapienza University of Rome, Rome, Italy.
    Montesarchio, Valeria
    Sapienza University of Rome, Rome, Italy.
    Rianna, Maura
    Sapienza University of Rome, Rome, Italy.
    Sebastianelli, Stefano
    Sapienza University of Rome.
    Russo, Fabio
    Sapienza University of Rome.
    Napolitano, Francesco
    Sapienza University of Rome.
    Evaluation of rainfall thresholds through entropy: Influence of bivariate distribution selection2013Inngår i: Irrigation and Drainage, ISSN 1531-0353, E-ISSN 1531-0361, Vol. 62, nr S2, s. 50-60Artikkel i tidsskrift (Fagfellevurdert)
  • 20.
    Ridolfi, Elena
    et al.
    Sapienza University of Rome, Rome, Italy.
    Montesarchio, Valeria
    Sapienza University of Rome, Rome, Italy.
    Russo, Fabio
    Sapienza University of Rome.
    Napolitano, Francesco
    Sapienza University of Rome.
    An entropy approach for evaluating the maximum information content achievable by an urban rainfall network2011Inngår i: Natural hazards and earth system sciences, ISSN 1561-8633, E-ISSN 1684-9981, Vol. 11, nr 7, s. 2075-2083Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Hydrological models are the basis of operational flood-forecasting systems. The accuracy of these models is strongly dependent on the quality and quantity of the input information represented by rainfall height. Finer space-time rainfall resolution results in more accurate hazard forecasting. In this framework, an optimum raingauge network is essential in predicting flood events.This paper develops an entropy-based approach to evaluate the maximum information content achievable by a rainfall network for different sampling time intervals. The procedure is based on the determination of the coefficients of transferred and nontransferred information and on the relative isoinformation contours.The nontransferred information value achieved by the whole network is strictly dependent on the sampling time intervals considered. An empirical curve is defined, to assess the objective of the research: the nontransferred information value is plotted versus the associated sampling time on a semi-log scale. The curve has a linear trend.In this paper, the methodology is applied to the high-density raingauge network of the urban area of Rome.

  • 21.
    Ridolfi, Elena
    et al.
    Univ Perugia, DICA, I-06100 Perugia, Italy..
    Rianna, M.
    Sapienza Univ Roma, DICEA, Rome, Italy..
    Trani, G.
    Sapienza Univ Roma, DICEA, Rome, Italy..
    Alfonso, L.
    UNESCO IHE, Hydroinformat Chair Grp, Delft, Netherlands..
    Di Baldassarre, Giuliano
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Geovetenskapliga sektionen, Institutionen för geovetenskaper, Luft-, vatten och landskapslära.
    Napolitano, F.
    Sapienza Univ Roma, DICEA, Rome, Italy..
    Russo, F.
    Sapienza Univ Roma, DICEA, Rome, Italy..
    A new methodology to define homogeneous regions through an entropy based clustering method2016Inngår i: Advances in Water Resources, ISSN 0309-1708, E-ISSN 1872-9657, Vol. 96, s. 237-250Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    One of the most crucial steps in flow frequency studies is the definition of Homogenous Regions (HRs), i.e. areas with similar hydrological behavior. This is essential in ungauged catchments, as HR allows information to be transferred from a neighboring river basin. This study proposes a new, entropy-based approach to define HRs, in which regions are defined as homogeneous if their hydrometric stations capture redundant information. The problem is handled through the definition of the Information Transferred Index (ITI) as the ratio between redundant information and the total information provided by pairs of stations. The methodology is compared with a traditional, distance-based clustering method through a Monte Carlo experiment and a jack-knife procedure. Results indicate that the ITI-based method performs Well, adding value to current methodologies to define HRs.

  • 22.
    Ridolfi, Elena
    et al.
    Sapienza University of Rome, Rome, Italy.
    Servili, Filippo
    Sapienza University of Rome, Rome, Italy.
    Magini, Roberto
    Sapienza University of Rome, Rome, Italy.
    Artificial Neural Networks and entropy-based methods to determine pressure distribution in water distribution systems2014Konferansepaper (Fagfellevurdert)
  • 23.
    Ridolfi, Elena
    et al.
    Sapienza University of Rome, Rome, Italy.
    Vertommen, Ina
    epartment of Civil Engineering, University of Coimbra, Coimbra, Portugal.
    Magini, Roberto
    Sapienza University of Rome, Rome, Italy.
    Joint probabilities of demands on a water distribution network: A non-parametric approach2013Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper proposes aprocedure to determine the probability of a specific water demand scenario in a Water Distribution Network (WDN). Stochastic correlated demands are generated for each node of the network using scaling laws. In particular, each demand fits a normal probability density function (PDF). To determine the joint probability of water demands at all nodes of the network, each nodal demand is divided in class intervals and a multidimensional contingency table is built. The joint probability represents the occurrence probability of a specific water demand scenario. The presented approach produces valuable information about demand scenarios and their probability of occurrence in a network. This method can find a further application in the robust optimization models for the design and management of WDN.

  • 24.
    Ridolfi, Elena
    et al.
    Dipartimento di Idraulica, Trasporti e Strade - Università Sapienza , Rome, Italy.
    Yan, Kun
    IHE-Delft.
    Alfonso, Leonardo
    IHE-Delft.
    Di Baldassarre, Giuliano
    IHE-Delft.
    Napolitano, Francesco
    Sapienza University of Rome.
    Russo, Fabio
    Sapienza University of Rome.
    Bates, Paul D.
    University of Bristol, UK.
    An entropy method for floodplain monitoring network design2012Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In recent years an increasing number of flood-related fatalities has highlighted the necessity of improving flood risk management to reduce human and economic losses. In this framework, monitoring of flood-prone areas is a key factor for building a resilient environment. In this paper a method for designing a floodplain monitoring network is presented. A redundant network of cheap wireless sensors (GridStix) measuring water depth is considered over a reach of the River Dee (UK), with sensors placed both in the channel and in the floodplain. Through a Three Objective Optimization Problem (TOOP) the best layouts of sensors are evaluated, minimizing their redundancy, maximizing their joint information content and maximizing the accuracy of the observations. A simple raster-based inundation model (LISFLOOD-FP) is used to generate a synthetic GridStix data set of water stages. The Digital Elevation Model (DEM) that is used for hydraulic model building is the globally and freely available SRTM DEM.

  • 25.
    Spina, Sandra
    et al.
    Sapienza University of Rome.
    Sebastianelli, Stefano
    Sapienza University of Rome.
    Ridolfi, Elena
    Sapienza University of Rome.
    Russo, Fabio
    Sapienza University of Rome.
    Baldini, Luca
    stituto di Scienze dell'Atmosfera e Del Clima, Consiglio Nazionale Delle Ricerche, Rome, Italy.
    Alfonso, Leonardo
    IHE-Delft.
    Data selection to assess bias in rainfall radar estimates: An entropy-based method2013Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Miscalibration of radar determines a systematic error (i.e., bias) that is observed in radar estimates of rainfall. Although a rain gauge can provide a pointwise rainfall measurement, weather radar can cover an extended area. To compare the two measurements, it is necessary to individuate the weather radar measurements at the same location as the rain gauge. Bias is measured as the ratio between cumulative rain gauge measurements and the corresponding radar estimates. The rainfall is usually cumulated, taking into account all rainfall events registered in the target area. The contribution of this work is the determination of the optimal number of rainfall events that are necessary to calibrate rainfall radar. The proposed methodology is based on the entropy concept. In particular, the optimal number of events must fulfil two conditions, namely, maximisation of information content and minimisation of redundant information. To verify the methodology, the bias values are estimated with 1) a reduced number of events and 2) all available data. The proposed approach is tested on the Polar 55C weather radar located in the borough area of Rome (IT). The radar is calibrated against rainfall measurements of a couple of rain gauges placed in the Roman city centre. Analysing the information content of all data, it is found that it is possible to reduce the number of rainfall events without losing information in evaluating the bias.

1 - 25 of 25
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