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This is the talk given by Riccardo Rigon to the Department of Civil, Environmental and Mechanical Engineer, of University of Trento, for his call as Full Professor (Dec 16, 2015). It covers his past research on fractal river network, the hydrologic response, hydrogeomorphometry, high resolution -process-based hydrological modeling with GEOtop, large scale modeling with JGrass-NewAGE and future research directions

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  1. 1. Water and life a hydrological perspective of research Riccardo Rigon 16 December 2015 Whatdowecomefrom?Whatarewe?Wherearewegoing?-P.Gaugen1897
  2. 2. !2 Resembles Life what once was held of Light, Too ample in itself for human sight? … S. Coleridge
  3. 3. !3 1 PROOF 2 R. RIGON ET AL. Figure 1. A basin Q4subdivided into five HRUs and ‘exploded’ into paths. Any path can be further subdivided into parts, called ‘states’, and once each part is translated into mathematics the overall response is the sum over the parts, having assumed a linear behavior. The blue dots delineate the position of HRUs outlets. For instance, for HRU 1 the path is H1 ! c1 ! c2, and the travel time distribution is obtained by the convolution of the probability distribution function in states H1, c1 and c2, and analogously for the other paths. This figure is available in colour online at If an HRU is checked at an arbitrary time, a water molecule in the HRU will have a residence time, which is the time spent river courses, especially in the Tropics, were hardly known at all. Therefore, the paper also tried to use information about the shape and form of rivers, given by knowledge of Hor- ton’s law of bifurcation ratios, length ratios, area ratios and Schumm’s law of slopes (e.g. Rodríguez-Iturbe and Rinaldo, 1997; Cudennec et al., 2004). According to them, a river’s drainage structure could be summarized by only a few num- bers, mainly the bifurcation ratio and the length ratio: the first was used to describe the geometrical extension of the river network, and the second to provide the mean travel times in each part of the network. To move from the drainage structure to the hydrograph, a fundamental hypothesis had to be made: during floods the wave celerity could be consid- ered constant along the network, as supported by Leopold and Maddock (1953). In theory, the constancy of celerity was necessary only within each partition of the basin (i.e. in each HRU or state used for its disaggregation) and not in the overall network (as was actually done in many studies for practical purposes), and actually this assumption can be fully relaxed. Formally, the main equation summarizing all of this reads: Q.t/ D A Z t 0 p.t /Je. /d p.t/ D X 2€ p .p 1 p /.t/ (1) where A is the area of the basin, Je is the effective precip- itation (i.e. the part of precipitation that contributes to the discharge), p is the instantaneous unit hydrograph (i.e. the 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 afterRigonetal,2015 The theory of the Geomorphologic Unit Hydrograph. Starting from the simplest 1 Q(t) = A X 2 (Jeff ⇤ p 1 ⇤ · · · ⇤ p ⌦ )(t) R. Rigon
  4. 4. !4 1 Various elements here • A Lagrangian view of the runoff production (integrated at basin scale) • The geometry and topology of basins as part of the construction of the probabilities • The assessment the geometry counts more than the details of the dynamics in generating the flood wave shape • The view of basins as fractal geometries • some analytic result 2 A little change in some paradigm R. Rigon
  5. 5. !5 2 WATER RESOURCESRESEARCH,VOL. 28,NO. 4, PAGES 1095-1103,APRIL 1992 EnergyDissipation,RunoffProduction,and the Three-Dimensional Structure of River Basins IGNACIORODRfGUEZ-ITURBE,I,2ANDREARINALDO,3RICCARDORIGON,'* RAFAELL. BRAS,2ALESSANDROMARANI,4 AND EDE IJJ/(Sz-VXSQUEZ2 Threeprinciplesof optimalenergyexpenditureare usedto derivethe mostimportantstructural characteristicsobservedindrainagenetworks:(I) theprincipleofminimumenergyexpenditureinany linkofthenetwork,(2)theprincipleofequalenergyexpenditureperunitareaof channelanywherein the network,and(3) the principleof minimumtotal energyexpenditurein the networkas a whole. Theirjoint applica,tionresultsin a unifiedpictureof themostimportantempiricalfactswhichhave beenobservedin thedynamicsof thenetworkanditsthree-dimensionalstructure.They alsolink the processof runoffproductionin thebasinwiththecharacteris.ticsof the network. INTRODUCTION' THE CONNECTIVITY ISSUE Well-developedriver basinsare made up of two interre- latedsystems'the channelnetwork and the hillslopes.The hillslopescontrolthe productionof runoffwhichin turn is transportedthroughthe channelnetworktowardthe basin outlet.Every branch of the network is linked to a down- streambranchfor the transportation of water and sediment butit is also linked for its viability, throughthe hillslope system,toevery otherbranchin the basin.Hillslopesarethe runoff-producingelements which. the n.etwork connects, transformingthe spatially distributedpotential ,energyaris- ingfromrainfallin the hillslopesto kineticenergyin theflow throughthe channelreaches. In this paper we focuson the drainagenetwork as it is controlled by energy dissipation principles.It !spreciselytheneedfor effectiveconnectivity thatleadsto the treelike structureof the drainagenetwork. Figure!, from Stevens[1974], illustratesthis point. Assume onewishestoconnectasetofpointsinaplanetoacommon outletandfor illustrationpu.rposesassumethat everypoint isequallydistantfrom its nearestneighbors.Two extreme case each individualis supposedto operate at his best completelyobliviousof his neighbors,but the systemas a whole cannot survive. Branchingpatterns accomplish connectivity combining thebestof thetwo extremes;they are shortaswell asdirect. The drainagenetwork, as well as many other natural con- nectingpat.terns, is basically a transportationsygtemfor which the treelike structure is a most appealing structure from the point of view of efficiency in the construction, operation and maintenance of the system. The drainage network accomplishes connectivity for transportationin three dimensions working against a resis- tance force derived from the friction of the flow with the bottomandbanksof the channels, the resistanceforce being itself a function of the flow and the channel characteristics. This makesthe analysisof the optimal connectivity a com- plex problem that cannot be separated from the individual optimalchannelconfigurationandfrom .thespatialcharac- terization of the runoff production inside the basin. The questionis whethertherearegeneralprinciplesthatrelate thestructureof the network and its individualelementsWith If geometry counts, from where geometry comes from ? 1096 RODFffGUEZ-ITURBEET AL,' STRUCTUREOF DRAINAGE NETWORKS 233.1,•--303,3 L- 3.73 Fig. 1. Different patterns of connectivity of a set of equally spacedpointstoa commonoutlet.L r isthetotallengthof thepaths, andL is the averagelengthof the pathfrom a pointto the outlet. In theexplosioncase,L•2)referstothecasewhenthereisaminimum displacementamong the points so that there is a different path betweeneachpoint and the outlet [from Stevens,1974]. network; (2) the principle of equal energy expenditureper unit area of channel anywhere in the network; and (3) the principleof minimumenergyexpenditurein the networkas a whole. It will be shown that the combination of these principlesis a sufficientexplanationfor the treelike structure of the drainagenetwork and, moreover, that they explain equalthesumofthecubesoftheradiiofthedaughter vessels(see,forexample,Sherman[1981]).Heassumedthat twoenergytermscontributetothecostofmaintainingblood flowin anyvessel:(1) theenergyrequiredto overcome frictionasdescribedbyPoiseuille'slaw,and(2)theenergy metabolicallyinvolvedin the maintenanceof theblood volumeandvesseltissue.Minimizationofthecostfuncfi0a leadstotheradiusofthevesselbeingproportionaltothelB powerof the flow. Uylings[1977]hasshownthatwhen turbulentflowisassumedinthevessel,ratherthanlain'mar conditions,thesameapproachleadstotheradiusbe'rag proportionalto the 3/7 power of the flow. The secorot principlewasconceptuallysuggestedbyLeopoldandLang. bein[1962]in theirstudiesof landscapeevolution.It isof interestto addthatminimumrate of workprincipleshave been appliedin severalcontextsin geomorphicresearch. Optimaljunctionangleshavebeenstudiedinthiscontextby Howard[1971],Roy [1983],andWoldenbergandHorsfield [1986],amongothers.Also the conceptof minimumworkas a criterion for the developmentof streamnetworkshasbeen discussedunder differentperspectivesby Yang[1971]a•d Howard [1990], amongothers. ENERGY EXPENDITURE AND OPTIMAL NETWORK CONFIGURATION Considera channelof width w, lengthL, slope$, andflow depthd. The forceresponsiblefor theflowisthedownslope componentof the weight, F1 = ptldLw sin /3 = ptIdLwS where sin/3 = tan/3 = S. The force resistingthemovement is the stressper unit area times the wetted perimeterarea, F2 = •(2d + w)L, where a rectangularsectionhasbeen assumed in the channel. Under conditions of no acceleration of the flow, F1 = F 2, and then r = p.qSRwhereR isthe hydraulicradiusR = Aw/Pw = wd/(2d + w), Awand beingthe cross-sectionalflow area, andthewettedperimeter ofthesection,respectively.In turbulentincompressibleflow theboundaryshearstressvariesproportionallytothesqua• oftheaveragevelocity,r = Cfpv2,whereCfisadimen. sionlessresistancecoefficient.Equatingthetwoexpressions for,, oneobtainsthewell-knownrelationship,S= Cfv2/ (R•/),whichgivesthelossesduetofrictionperunitweightof flowperunitlengthofchannel.Thereisalsoanexpendi• 1 Why river are more like this instead that in other forms ? E = argmin Configurations ( X i2all sites Ai ) R. Rigon
  6. 6. !6 2 Evolution and selection of river networks: Statics, dynamics and complexity Andrea Rinaldo ∗ † , Riccardo Rigon ‡ , Jayanth R. Banavar § , Amos Maritan ¶ , and Ignacio Rodriguez-Iturbe ∥ ∗ Laboratory of Ecohydrology ECHO/IIE/ENAC, ´Ecole Polytechnique F´ed´erale Lausanne EPFL, Lausanne CH-1015, CH,† Dipartimento IMAGE, Universit´a di Padova, I-35131 Padova, Italy,‡ Dipartimento di Ingegneria Civile e Ambientale, Universit`a di Trento, Italy,§ Department of Physics, University of Maryland, College Park, Maryland 20742, USA,¶ Dipartimento di Fisica e INFN, Padova, Italy, and ∥ Department of Civil and Environmental Engineering, Princeton University This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected on May 1, 2012 (AR). Moving from the exact result that drainage network configurations minimizing total energy dissipation are stationary solutions of the general equation describing landscape evolution, we review the static properties and the dynamic origins of the scale-invariant structure of optimal river patterns. Optimal Channel Networks (OCNs) are fea- sible optimal configurations of a spanning network mimicking land- Rather, each of them can be derived through scaling relations postulating the knowledge of geometrical constraints. And, as is common in any good detective novel, our story comes with unexpected twists. The first surprise was that the observa- tional exponents do not fall into any known standard univer- sality class of spanning or directed trees with equal weight. A General principles acting 22 • The main idea here is that river networks forms on the basis of minimal energy expenditure • Maximum Entropy and minimal energy are in fact principles acting on a large set of systems whose functioning can be attributed to some “network” connectivity • This is still an open question in literature … PNAS, 2014 Ideas behind R. Rigon
  7. 7. !7 32 13 April, 1995 Self-Organisation or how forms emerge and are continuously destroyed by diffusion Self organising criticality ? And its destruction R. Rigon
  8. 8. !8 Many evidences showed that it could be the same Same as optimality ? R. Rigon
  9. 9. !9 On Hack’s law Riccardo Rigon,1,2 Ignacio Rodriguez-Iturbe,1 Amos Maritan,3 Achille Giacometti,4 David G. Tarboton,5 and Andrea Rinaldo6 Abstract. Hack’s law is reviewed, emphasizing its implications for the elongation of river basins as well as its connections with their fractal characteristics. The relation between Hack’s law and the internal structure of river basins is investigated experimentally through digital elevation models. It is found that Hack’s exponent, elongation, and some relevant fractal characters are closely related. The self-affine character of basin boundaries is shown to be connected to the power law decay of the probability of total contributing areas at any link and to Hack’s law. An explanation for Hack’s law is derived from scaling arguments. From the results we suggest that a statistical framework referring to the scaling invariance of the entire basin structure should be used in the interpretation of Hack’s law. 1. Introduction Hack [1957] demonstrated the applicability of a power func- tion relating length and area for streams of the Shenandoah Valley and adjacent mountains in Virginia. He found the equa- tion L 5 1.4A0.6 (1) where L is the length of the longest stream in miles from the outlet to the divide and A is the corresponding area in square miles. Hack also corroborated his equation through the mea- surements of Langbein [1947], who had measured L and A for nearly 400 sites in the northeastern United States. Gray [1961] later refined the analysis, finding a relationship L } A0.568 . Many other researchers have corroborated Hack’s original study, and, although the exponent in the power law may slightly vary from region to region, it is generally accepted to be slightly below 0.6. Equation (1) rewritten as L } Ah with h . 0.5 is usually termed “Hack’s law.” Muller [1973], on the basis of extensive data analysis of several thousand basins, found that the exponent in Hack’s equation was not constant but that it changed from 0.6 for basins less than 8,000 square miles (20,720 km2 ) to 0.5 for basins between 8,000 and 105 square miles (20,720–259,000 km2 ), and to 0.47 for basins larger than 105 square miles (259,000 km2 ). As Mesa and Gupta [1987] point out, Muller’s empirical observations are not consistent with the implications of the troduced in the classic paper of Shreve [1966]. In fact, they theoretically derived the value of Hack’s exponent, h, for the random topology model of channel networks as h~n! 5 1 2 Sp 1 ~p/n!1/ 2 p 2 1/n D (2) where n is the basin’s magnitude. Equation (2) implies a con- tinuously decreasing h(n) with an increasing n. For n 5 10,100, and 500 the exponent h(n) is 0.68, 0.530, and 0.513, respectively. When n tends to infinity, h tends to the asymp- totic value of 0.5. This result makes clear the importance of the magnitude of the network in the exponent h under the pre- mises of the random topology model. Further and more gen- eral results on random trees can also be found in work by Durret et al. [1991]. The classical explanation for the exponent h being larger than 0.5 was to conjecture that basins have anisotropic shapes and tend to become narrower as they enlarge or elongate. The hypothesis of basin elongation was verified by Ijjasz-Vasquez et al. [1993] under the framework of optimal channel networks (OCNs), which are the result of the search of fluvial systems for a drainage configuration whose total energy expenditure is minimized [Rodriguez-Iturbe et al., 1992a; Rinaldo et al., 1992]. Thus Hack’s relationship may result from the competition and minimization of energy in river basins. Mandelbrot [1983] suggested that an exponent larger than 0.5 in L } Ah could arise from the fractal characters of river channels which cause the measured length to vary with the WATER RESOURCES RESEARCH, VOL. 32, NO. 11, PAGES 3367–3374, NOVEMBER 1996 4 Back almost from where we started Misura ciò che e misurabile e rendi misurabile ciò che non lo è. Measure what is measurable and make measurable what is not Galileo Galilei pretation of the empirical evidence. Specifically, we focus on the internal structure of basins whose extension is in the range of 50–2000 km2 . Theoretical and experimental motivations justify this choice. At lower scales, diffusive processes interact with concentrative erosive processes responsible for concave landforms, and area-length relationships are altered. At very large scales geologic controls dominate. We expect instead that at medium to small scales, self-organization plays a predomi- nant role, yielding the observed recurrent characters of river basins. Furthermore, Montgomery and Dietrich’s [1992] collec- tion of data shows that a composite data set, from 100 m2 up to 107 km2 , can reasonably be fitted with an exponent of 0.5 in Hack’s relation, and hence a large span of orders of magnitude in basin area is not the most adequate to fit as a whole when investigating Hack’s equation. 2. Does Hack’s Law Imply Elongation? This section considers the connection between Hack’s law, the fractal sinuosity of stream channels, and the elongation of river basins. The meaning of the terms “elongation” and “frac- tal sinuosity” first needs to be defined. The planar projection of river basins may be characterized by Shapes will be c for all areas, A, Alternatively, if constant, basin constant we no Constant a(L) creasing with A One interpre along channels while s remains h 5 0.57: This suggests t that according t Another inte brot [1983] is th stream length, L where fL is a assumed to be s and thus L } A The more gene streams are fra watershed shap nent H [e.g., M 1993]: where H , 1, and a(L) beco For H , 1, a( gation. Using (1 which combined Thus we have [ which relates se fL, and Hack’s Maritan et al. [1 differs from pre Figure 1. Sketch of a river basin; its diameter, L; and its width, L'. Some subbasins are also drawn. For any subbasin the longest sides of the rectangle enclosing the network are parallel to the diameter L, defined as the straight line from the outlet to the farthest point in the basin. The shortest sides are L'. RIGON ET AL.: ON HACK’S LAW3368 L = ↵A 1 R. Rigon
  10. 10. !10 42 In this case measuring is measuring terrain. The tools are Digital Elevation Models … and GISes British Society for Geomorphology Geomorphological Techniques, Chap. X, Sec. X (2012) associated properties such as the starting and ending point’s of a link, elevation drop to determine average slope of each links, etc. The example of pfafstteter coding scheme for channel and hillslope is provided in figure 3 for Posina river basin in North East Italy. Figure 3: The pfafstetter enumeration scheme in uDig GIS spatial toolbox for channel networks and hillslopes for Posina river basin in Northaest Italy 3.4 Hillslope toolbox The tools in Hillslope menu are presented in transversal curvatures, topographic class (Tc) tool subdivides the sites of a basin in different topographic classes. The program has two outputs: the more detailed 9 topographic classes (Parsons, 1988) and an aggregated topographic class with three fundamental classes. Planar curvature represents the degree of divergence or convergence perpendicular to the flow direction, and profile curvature shows convexity or concavity along the flow direction. By combing these two landform curvatures, topographic class (Tc) tools produce 9 classes, which are three types of planar (parallel–planar, divergent-planar, convergent-planar sites), three types of convex (parallel-convex, divergent-convex, and convergent-convex sites), and three concave (divergent-concave, parallel- concave, and convergent-concave sites). These attributes can be summarized just in With I did a few GIS (now all is being ported in GVsig) TheuDigSpatialToolboxforhydro-geomorphicanalysisby computer RiccardoRigon1 ,AndreaAntonello2 ,SilviaFranceschi2 ,WuletawuAbera1 ,Giuseppe Formetta3 1 DepartmentofCivil,Environmental,andMechanicalEngineering,TrentoUniversity,Italy ( 2 Hydrologiss.r.l.ViaSiemens,19Bolzano( 3 UniversityofCalabria,Calabria,Italy( ABSTRACT:Geographicalinformationsystems(GIS)arenowwidelyusedinhydrologyand geomorphologytoautomatebasin,hillslope,andstreamnetworkanalyses.Severalcommercial GISpackageshaveincorporatedmorecommonterrainattributesandterrainanalysisprocedures. Thesesoftwarepackagesare,however,oftenprohibitivelyexpensive.JGrasstoolsinuDigGIS insteadisfreeandOpenSource.uDigisanopensourcedesktopapplicationframework,builtwith EclipseRichClient(RCP)technology,whichismainlyforsoftwareandmodelbuildingcommunity. However,recentlyuDigGISaddedsignificantresourcesforenvironmentalanalysis.Spatial toolboxofuDigGISisaspecializedGIStoolsfortheanalysisoftopographyforgeomorphometry andhydrology.Largenumbersoftoolsareembeddedinthetoolboxforterrainanalysis,river networkdelineation,andbasintopologycharacterization,andaredesignedtomeettheresearch needsforacademicscientistswhilebeingsimpleenoughinoperationtobeusedforstudent instructionandprofessionaluse.JGrasstoolsanduDigaredevelopedinJavathatensurethe portabilityinalloperatingsystemsrunningaJavaVirtualMachine.Theaimofthispaperisto presenttheSpatialtoolboxofuDigGISforgeomorphologicalstudy. KEYWORDS:Hydrology,geomorphology,GIS,OpenSource,catchmentanalysis,network extraction No more without a GIS ? R. Rigon
  11. 11. !11 GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets RICCARDO RIGON AND GIACOMO BERTOLDI Department of Civil and Environmental Engineering, CUDAM, University of Trento, Trento, Italy THOMAS M. OVER Department of Geology/Geography, Eastern Illinois University, Charleston, Illinois (Manuscript received 1 March 2005, in final form 11 August 2005) ABSTRACT This paper describes a new distributed hydrological model, called GEOtop. The model accommodates very complex topography and, besides the water balance, unlike most other hydrological models, integrates all the terms in the surface energy balance equation. GEOtop uses a discretization of the landscape based on digital elevation data. These digital elevation data are preprocessed to allow modeling of the effect of topography on the radiation incident on the surface, both shortwave (including shadowing) and longwave (accounting for the sky view factor). For saturated and unsaturated subsurface flow, GEOtop makes use of a numerical solution of the 3D Richards’ equation in order to properly model, besides the lateral flow, the vertical structure of water content and the suction dynamics. These characteristics are deemed necessary for consistently modeling hillslope processes, initiation of landslides, snowmelt processes, and ecohydrological phenomena as well as discharges during floods and interstorm periods. An accurate treatment of radiation inputs is implemented in order to be able to return surface temperature. The motivation behind the model is to combine the strengths and overcome the weaknesses of flood forecasting and land surface models. The former often include detailed spatial description and lateral fluxes but usually lack appropriate knowledge of the vertical ones. The latter are focused on vertical structure and usually lack spatial structure and prediction of lateral fluxes. Outlines of the processes simulated and the methods used to simulate them are given. A series of applications of the model to the Little Washita basin of Oklahoma using data from the Southern Great Plains 1997 Hydrology Experiment (SGP97) is presented. These show the model’s ability to reproduce the pointwise energy and water balance, showing that just an elementary calibration of a few parameters is needed for an acceptable reproduction of discharge at the outlet, for the prediction of the spatial distribution of soil moisture content, and for the simulation of a full year’s streamflow without additional calibration. 1. Introduction: Design prerequisites The study of river basin hydrology is focused on the analysis of the interactions between the near-surface soil and the atmospheric boundary layer (ABL), which occur mainly through the mediation of the soil itself, the vegetation, and the turbulent and radiative energy transfers taking place on the earth’s surface, and pos- sible feedbacks from the ABL (e.g., soil moisture– and momentum exchanges between the land surface and the atmosphere at several scales (Abbott 1992; Reggiani et al. 1999), with the purpose of creating mod- els that can provide improved mid- and long-term hy- drologic forecasts and better prediction of the impacts on the hydrologic cycle and on the earth’s ecosystems resulting from changes in land use and in the climate (Grayson and Blöschl 2000). Though inspired by this trend toward improved predictions, the initial motiva- JUNE 2006 R I G O N E T A L . 371 Geosci. Model Dev., 7, 2831–2857, 2014 doi:10.5194/gmd-7-2831-2014 © Author(s) 2014. CC Attribution 3.0 License. GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects S. Endrizzi1, S. Gruber2, M. Dall’Amico3, and R. Rigon4 1Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland 2Carleton University, Department of Geography and Environmental Studies, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada 3Mountaineering GmbH, Siemensstrasse 19, 39100 Bozen, Italy 4Dipartimento di Ingegneria Civile, Ambientale e Meccanica e CUDAM, Università di Trento, Via Mesiano 77, 38123 Trento, Italy Correspondence to: S. Endrizzi ( Received: 4 October 2013 – Published in Geosci. Model Dev. Discuss.: 3 December 2013 Revised: 25 September 2014 – Accepted: 30 September 2014 – Published: 3 December 2014 A second thread Hyperresolution hydrological modeling 61 R. Rigon
  12. 12. !12 What the hell are you doing ? After a decade of smart models of river networks and papers on river hydro-geomorphology Rigon seems to have abandoned simplicity and creativity, for choosing overcomplicate machineries based on a mechanistic view of the world. Is, probably, a sign of decline. (No good research after 45 ?) But overall, what I'm craving? A little perspective. Anton Egò A debate 62 R. Rigon
  13. 13. !13 JackCook,WoodsHoleOceanographicInstitution How much water there is on Earth ? Around 1400 millions of km3 Shiklomanov and Skolov (1983) A flash back R. Rigon
  14. 14. !14 Collocazione Area coperta Volume % % delle acque [106 km2 ] [106 km3 ] dolci Oceani 361.300 1.338 96.5 - Acque di falda 134.8 23.4 1.7 - Acque di falda dolci 10.530 0.76 30.1 Umidit`a del suolo 82 0.0165 0.001 0.05 Ghiacci e neve perenni 16.2275 24.0641 1.74 68.7 Antartico 13.980 21.600 1.56 61.7 Groenlandia 1.8024 2.340 0.17 6.68 Isole artiche 0.2261 0.0835 0.006 0.24 Aree montane 0.224 0.0406 0.003 0.12 Permafrost 21 0.3 0.022 0.86 Acque nei laghi 2.0587 0.1764 0.013 - Acque dolci nei laghi 1.2364 0.091 0.007 0.26 Acque salate nei laghi 0.8223 0.0854 0.006 - Lagune e paludi 2.682.6 0.01147 0.0002 0.006 Acque nei fiumi 148.8 0.00212 0.0002 0.0006 Acqua negli esseri viventi 510 0.0012 0.0.0001 0.0003 Acqua nell’atmosfera 510 0.0129 0.001 0.04 Totale d’acqua 510 1385.98561 100 - Totale d’acqua dolce 148.8 35.02921 2.53 100 From: Global Change in the Geosphere-Biosphere, NRC, 1986, Shiklomanov and Skolov (1983). But also: Oki et al., 2001; Shiklomanov, I. A., 2000; Vorosmarty et al., 2000; Hanasaki et al., 2006 Hydrological storages Numbers R. Rigon
  15. 15. !15 modificatodaWallaceandHobbs,1977Energy R. Rigon between 0.2 and 4, e.g.. Smil, 2003 possible ~ 10 and 11 respectively over land masses, after L’Ecuyer et al., 2015
  16. 16. !16 OkiandKanae,2006 Fluxes and Interactions R. Rigon πάντα ῥεῖ A very recent: Rodell et al., 2015
  17. 17. regulates the climate !17 sustains life on Earth sculpt Earth’s surfaces The hydrological cycle it is at the origin of fundamental ecosystem services Why it is important R. Rigon
  18. 18. !18 Venus Earth Mars 96.5% CO2 3.5% N2 93.5% CO2 2.7% N2 78 % N2 31% O2 However Entanglements R. Rigon
  19. 19. !19 Studies on photosynthesis say that O2 is produced by plants splitting the water molecule, while carbon dioxide oxygen is fixed in plants themselves So life creates Earth atmosphere and the hydrological cycle we see today Other’s planets has a very different atmosphere Entanglements and feedbacks R. Rigon
  20. 20. !20 Dear Anton: You asked for a little perspective*, which I take seriously. So far surface hydrology modelling was essentially estimating discharges Now is: • water mass conservation • energy conservation • appropriate momentum treatment As proper to any physical science So 63 R. Rigon * Quotes
  21. 21. !21 64 This was also a way to cope with the entire terrestrial water cycle, and the whole set of processes according to the basic known laws How can we deal with nonlinear feedbacks if we linearised all the interactions ?
  22. 22. !22 GEOtop: a distributed model process based model for the remote sensing era - Princeton 2004 explained after Dietrich et al. 2003 R. Rigon Does it correspond to realism ? HenriRosseau,TheDream,1910
  23. 23. !23 Richards equation + van Genuchten parameterization + Mualem derived conductivity Energybudget (withsomeassumptions) Flux-gradient relationship (Monin - Obukov) Diffusive approximation to shallow water equation Double layer vegetation Radiation Snowmetamorphism Many Equations R. Rigon
  24. 24. !24 Se := w r ⇥s r C(⇥) := ⇤ w() ⇤⇥ Se = [1 + ( ⇥)m )] n ~Jv = K(✓w)~r h K( w) = Ks ⇧ Se ⇤ 1 (1 Se)1/m ⇥m⌅2 <latexitsha1_base64="tYHCApFiY8slQcKMwQxwGacE74A=">AAAA+3icSyrIySwuMTC4ycjEzMLKxs7BycXNw8XFy8cvEFacX1qUnBqanJ+TXxSRlFicmpOZlxpaklmSkxpRUJSamJuUkxqelO0Mkg8vSy0qzszPCympLEiNzU1Mz8tMy0xOLAEKBcQLKBvoGYCBAibDEMpQZoACoHJDdElMRqiRnpmeQSBCG4e0koahuYNHQGhyStfknfsPQoQZGaHyggyo4BQAVIE48g==</latexit> <latexitsha1_base64="tYHCApFiY8slQcKMwQxwGacE74A=">AAAA+3icSyrIySwuMTC4ycjEzMLKxs7BycXNw8XFy8cvEFacX1qUnBqanJ+TXxSRlFicmpOZlxpaklmSkxpRUJSamJuUkxqelO0Mkg8vSy0qzszPCympLEiNzU1Mz8tMy0xOLAEKBcQLKBvoGYCBAibDEMpQZoACoHJDdElMRqiRnpmeQSBCG4e0koahuYNHQGhyStfknfsPQoQZGaHyggyo4BQAVIE48g==</latexit> <latexitsha1_base64="tYHCApFiY8slQcKMwQxwGacE74A=">AAAA+3icSyrIySwuMTC4ycjEzMLKxs7BycXNw8XFy8cvEFacX1qUnBqanJ+TXxSRlFicmpOZlxpaklmSkxpRUJSamJuUkxqelO0Mkg8vSy0qzszPCympLEiNzU1Mz8tMy0xOLAEKBcQLKBvoGYCBAibDEMpQZoACoHJDdElMRqiRnpmeQSBCG4e0koahuYNHQGhyStfknfsPQoQZGaHyggyo4BQAVIE48g==</latexit> <latexit sha1_base64="tYHCApFiY8slQcKMwQxwGacE74A=">AAAA+3icSyrIySwuMTC4ycjEzMLKxs7BycXNw8XFy8cvEFacX1qUnBqanJ+TXxSRlFicmpOZlxpaklmSkxpRUJSamJuUkxqelO0Mkg8vSy0qzszPCympLEiNzU1Mz8tMy0xOLAEKBcQLKBvoGYCBAibDEMpQZoACoHJDdElMRqiRnpmeQSBCG4e0koahuYNHQGhyStfknfsPQoQZGaHyggyo4BQAVIE48g==</latexit> <latexit sha1_base64="tYHCApFiY8slQcKMwQxwGacE74A=">AAAA+3icSyrIySwuMTC4ycjEzMLKxs7BycXNw8XFy8cvEFacX1qUnBqanJ+TXxSRlFicmpOZlxpaklmSkxpRUJSamJuUkxqelO0Mkg8vSy0qzszPCympLEiNzU1Mz8tMy0xOLAEKBcQLKBvoGYCBAibDEMpQZoACoHJDdElMRqiRnpmeQSBCG4e0koahuYNHQGhyStfknfsPQoQZGaHyggyo4BQAVIE48g==</latexit> <latexit sha1_base64="tYHCApFiY8slQcKMwQxwGacE74A=">AAAA+3icSyrIySwuMTC4ycjEzMLKxs7BycXNw8XFy8cvEFacX1qUnBqanJ+TXxSRlFicmpOZlxpaklmSkxpRUJSamJuUkxqelO0Mkg8vSy0qzszPCympLEiNzU1Mz8tMy0xOLAEKBcQLKBvoGYCBAibDEMpQZoACoHJDdElMRqiRnpmeQSBCG4e0koahuYNHQGhyStfknfsPQoQZGaHyggyo4BQAVIE48g==</latexit> Many Equations R. Rigon
  25. 25. !25 Many Results R. Rigon 0 5 10 15 20 25 30 Discharge [m ³/s] Precipitation [m m ] 11.2009 01.2010 03.2010 11.2010 05.2010 07.2010 09.2010 40 30 20 10 0 m easured sim ulated Figure 5: Sim ulated and m easured discharge atthe gauge in Raisting forthe hydrologic year2010. 0 5 10 15 20 25 30 Discharge [m ³/s] Precipitation [m m ] 01.2011 03.2011 11.2011 05.2011 07.2011 09.2011 40 30 20 10 0 m easured sim ulated d m easured dischargeatthegaugeRaisting forthehydrologicyear 36 a) c) Hingerletal.,2013
  26. 26. !26 HYDROLOGICAL PROCESSES Hydrol. Process. 18, 3667–3679 (2004) Published online in Wiley InterScience ( DOI: 10.1002/hyp.5794 The GEOTOP snow module Fabrizio Zanotti, Stefano Endrizzi, Giacomo Bertoldi and Riccardo Rigon* Department of Civil and Environmental Engineering CUDAM, Universit`a di Trento, Trento, Italy Abstract: A snow accumulation and melt module implemented in the GEOTOP model is presented and tested. GEOTOP, a distributed model of the hydrological cycle, based on digital elevation models (DEMs), calculates the discharge at the basin outlet and estimates the local and distributed values of several hydro-meteorological quantities. It solves the energy and the mass balance jointly and deals accurately with the effects of topography on the interactions among radiation physics, energy balance and the hydrological cycle. Soil properties are considered to depend on soil temperature and moisture, and the heat and water transfer in the soil is modelled using a multilayer approach. The snow module solves for the soil–snow energy and mass exchanges, and, together with a runoff production module, is embedded in a more general energy balance model that provides all the boundary conditions required. The snowpack is schematized as a single snow layer where a limited number of physical processes are described. The module can be seen essentially as a parameter-free model. The application to an alpine catchment (Rio Valbiolo, Trentino, Italy), monitored by an in situ snow-depth sensor, is discussed and shown to give results comparable to those of more complex models. Copyright © 2004 John Wiley & Sons, Ltd. KEY WORDS snow; snowmelt; distributed modelling; energy balance INTRODUCTION A suitable model of the hydrological cycle of mountain catchments and basins located at higher latitudes must account for snow accumulation and melting and for soil freezing. The presence of snow modifies the energy and mass balances, and snowmelt is responsible for most of the runoff during the melting season. Snowmelt processes have been modelled with different approaches of variable complexity, ranging from simple methods based only on temperature measurements (Morris, 1985) to complete multilayer models based on an energy balance (Marks et al., 1999), like the one-dimensional US Army Cold Regions Research and Engineering Laboratory Model (SNTHERM; Jordan, 1991). This model makes use of a mixture theory to describe all the dry air, dry soil and water phases dynamics and thermal constituents, and it requires a large number of snow layers to be set and short integration intervals for the simulations. SNTHERM is a reference for the description of point processes (Jin et al., 1999), but owing to its complexity it is not suited to direct implementation within a distributed model of the hydrological cycle. In fact, it neglects all those phenomena related to lateral flows and surface conditions whose accurate description could be more important than that The Cryosphere, 5, 469–484, 2011 doi:10.5194/tc-5-469-2011 © Author(s) 2011. CC Attribution 3.0 License. The Cryosphere A robust and energy-conserving model of freezing variably-saturated soil M. Dall’Amico1,*, S. Endrizzi2, S. Gruber2, and R. Rigon1 1Department of Civil and Environmental Engineering, University of Trento, Trento, Italy 2Department of Geography, University of Zurich, Winterthurerstrasse 190, Zurich, Switzerland *now at: Mountain-eering srl, Via Siemens 19, Bolzano, Italy Received: 29 June 2010 – Published in The Cryosphere Discuss.: 11 August 2010 Revised: 18 May 2011 – Accepted: 19 May 2011 – Published: 1 June 2011 Abstract. Phenomena involving frozen soil or rock are im- portant in many natural systems and, as a consequence, there is a great interest in the modeling of their behavior. Few models exist that describe this process for both saturated and unsaturated soil and in conditions of freezing and thawing, as the energy equation shows strongly non-linear character- istics and is often difficult to handle with normal methods and numerical physically-based (Zhang et al., 2008). Em- pirical and semiempirical algorithms relate ground thawing- freezing depth to some aspect of surface forcing by one or more experimentally established coefficients (e.g. Anisimov et al., 2002). Analytical algorithms are specific solutions to heat conduction problems under certain assumptions. The most widely applied analytical solution is Stefan’s formula- 71 Cryospheric Processes ⇥w = ⇥r + (⇥s ⇥r) · ⇤ 1 + ⇤w0 Lf g T0 (T T⇥ ) · H(T T⇥ ) ⇥n⌅ m R. Rigon
  27. 27. !27 Si può misurare, si può prevedere … 27 R. Rigon
  28. 28. !28 Hydrological modelling with components: A GIS-based open-source framework G. Formetta a,*, A. Antonello b,1 , S. Franceschi b,1 , O. David c , R. Rigon a a Department of Civil, Enviromnental and Mechanical Engineering e CUDAM, 77 Mesiano St., Trento I-38123, Italy b Hydrologis S.r.l., Bolzano, BZ, Italy c Department of Civil and Environmental Engineering, Department of Computer Science, Colorado State University, Fort Collins, CO 80523, USA a r t i c l e i n f o Article history: Received 7 January 2013 Received in revised form 13 January 2014 Accepted 14 January 2014 Available online a b s t r a c t This paper describes the structure of JGrass-NewAge: a system for hydrological forecasting and modelling of water resources at the basin scale. It has been designed and implemented to emphasize the comparison of modelling solutions and reproduce hydrological modelling results in a straightforward manner. It is composed of two parts: (i) the data and result visualization system, based on the Geographic Information System uDig and (ii) the component-based modelling system, built on top of the Object Modelling System v3. Modelling components can be selected, adapted, and connected according Contents lists available at ScienceDirect Environmental Modelling & Software journal homepage: Environmental Modelling & Software 55 (2014) 190e200 One Lesson Learned from GEOtop and GIS research GEOtop code has a mature C++ implementation of solid algorithms and physics. However it is conceived as a monolithic structure, in which improvements can be made with difficulty and after overcoming a huge learning curve. At the same time, the user experience is far by being optimal, and must be structurally improved. Therefore, during the same evolution of the model, it was envisioned to migrate it towards a more flexible informatics where improvements, maintenance and documentation and research reproducibility could be pursued more easily. Informatics for Hydrology (and geoscience)9 The manifesto (mostly still valid) is here. R. Rigon
  29. 29. !29 10 Upscaling Does it means you want more money ? (An EU officier at the Aquaterra defence in Bruxelles) NO. It means we want • t o s i m u l a t e l a r g e b a s i n s , w i t h h u m a n infrastructure besides the natural complexity. It requires • the implementation and testing of new physical- statistical models. 1 R. Rigon See also: Botter et al., 2010; Rinaldo et al., 2015
  30. 30. !30 A physical-statistical theory of the Hydrologic cycle 102 Adige River R. Rigon
  31. 31. !31 A physical-statistical theory of the Hydrologic cycle 10 Blue Nile 3 R. Rigon
  32. 32. !32 RRECTED PROOF Journal Code: Article ID Dispatch: 17.11.15 CE: E S P 3 8 5 5 No. of Pages: 11 ME: EARTH SURFACE PROCESSES AND LANDFORMS Earth Surf. Process. Landforms (2015) Copyright © 2015 John Wiley & Sons, Ltd. Published online in Wiley Online Library ( DOI: 10.1002/esp.3855 State of Science The geomorphological unit hydrograph from a historical-critical perspective Riccardo Rigon,1* Marialaura Bancheri,1 Giuseppe Formetta2 and Alban de Lavenne3 1 Dipartimento di Ingegneria Civile e Ambientale, Università di Trento, 38123 Trento, Italy 2 Civil and Environmental Engineering Department, School of Mines, Golden, CO 80401, USA 3 Faculté des Sciences etQ1 Technique, Géo-Hydrosystèmes Continentaux Received 17 September 2015; RevisedQ2 ; Accepted 6 October 2015 *Correspondence to: Riccardo Rigon, Dipartimento di Ingegneria Civile e Ambientale, Università di Trento, 38123 Trento, Italy. E-mail: ABSTRACT: In this paper we present a brief overview of geomorphological instantaneous unit hydrograph (GIUH) theories and analyze their successful path without hiding their limitations. The history of the GIUH is subdivided into three major sections The first is based on the milestone works of Rodríguez-Iturbe and Valdés (Water Resources Research 1979; 15(6): 1409–1420) and Gupta and Waymire (Journal of Hydrology 1983; 65(1–3): 95–123), which recognized that a treatment of water discharges with ‘travel times’ could provide a rich interpretation of the theory of the instantaneous unit hydrograph (IUH). We show how this was possible, what assumptions were made, which of these assumptions can be relaxed, and which have become obsolete and been discarded. The second section focuses on the width-function-based IUH (WFIUH) approach and its achievements in assessing the interplay of the topology and geometry of the network with water dynamics. The limitations of the WFIUH approach are described, and a way to work around them is suggested. Finally, a new formal approach to estimating the water budget by ‘travel times’, which derives from a suitable use of the water budget equation and some hypotheses, has been introduced and disentangled. Copyright © 2015 John Wiley & Sons, Ltd. KEYWORDS: Geomorphological Unit Hydrograph; Hydrologic Response; Travel time theories Introduction Here we discuss the evolution of the geomorphological unit hydrograph in its attempts to assess the interplay of geomor- of its parts or a group of hillslopes, that mathematical, phys- ical or computational arguments suggest to treat as a whole. For the purposes of modeling, each of these HRUs is consid- ered, at least initially, as an ‘atomic’ part of the basin in which 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 Almost back to the beginning 10 In this retrospective of the last 35 years of the geomorphological unit hydrograph, there is a seed for the next development of a large scale theory, according to travel times. 4 Q(t) p Q(t ⌧|t) = f(t ⌧|t)J(⌧)
  33. 33. !33 So what is furtherly next ? R. Rigon has to be quietly evolved. Numerics revised. Vegetation dynamics introduced. Informatics changed to the new paradigm of components. Alternative equations and parameterisations selected. Usability enhanced. Parallelism introduced. (Big) Data assimilation used. It is already a good model but: Towards 3.0
  34. 34. !34 computationally demanding. Therefore, several eco- hydrological models still use simplified solutions of carbon285 ) concepts that empirically link carbon assimilation to the transpired water or intercepted Energy exchanges Longwave radiation incoming Longwave radiation outgoing Shortwave radiation Latent heat Latent heat Sensible heat Soil heat flux Geothermal heat gain Bedrock Bedrock Bedrock Bedrock Momentum transfer Rain Snow Photosynthesis Phenology Disturbances Atmospheric deposition Fertilization Nutrient resorption Nutrient uptake Nutrients in SOM Mineral nutrients in solution Mineralization and immobilizationOccluded or not available nutrients Primary mineral weathering Biological fixation (N) Tectonic uplift Denitrification (N) Volatilization Growth respiration Maintenance respiration Fruits/flowers production Heterotrophic respiration Wood turnover Litter Litter Litterfall nutrient flux DecompositionMycorrhizal symbiosis Microbial and soil fauna activity SOM DOC leaching Leaching Fine and coarse root turnover Carbon allocation and translocation Carbon reserves (NSC) Leaf turnover Transpiration Evaporation from interception Evaporation/ sublimation from snow Evaporation Throughfall/dripping Snow melting Infiltration Leakage Root water uptake Lateral subsurface flow Base flow Deep recharge Runoff Sensible heat Albedo Energy absorbed by photosynthesis Water cycle Carbon cycle Nutrient cycle FIGURE 6 | Ecohydrological and terrestrial biosphere models have components and parameterizations to simulate the (1) surface energy exchanges, (2) the water cycle, (3) the carbon cycle, and (4) soil biogeochemistry and nutrient cycles. Many models do not include all the components presented in the figure. WIREs Water Modeling plant–water interactions More thermo-mechanistic ? So what is furtherly next ?afterFatichi,PappasandIvanov,2015 R. Rigon Maybe, but without forgetting the “less is more” lesson.
  35. 35. !352005 drought-afflicted ecohydrological system. The result- ing weighted-cut process network for July 2005 is visual- ized in Figure 8. The first salient observation is that the drought process has fewer couplings than the healthy process network; in fact, roughly half of the couplings disappear during the drought state (adjacency matrix re- drought because of insufficient information input from the synoptic weather patterns. The moisture fluxes which carry the information may be reduced below a key threshold during drought. [63] The absence of information flow from the ABL subsystem to the turbulent subsystem means that the circu- Figure 7. The process network for July 2003, a healthy system state. Types 1, 2, and 3 relationships result in the interpretation of the system as three subsystems linked at time scales ranging from 30 min to 12 h. Thin arrows represent type 2 couplings. Thick arrows represent type 3 couplings. A type 1 ‘‘synoptic’’ subsystem including GER, q, Qs, Qa, and VPD forces the other subsystems at all studied time scales from 30 min to 18 h. A type 2 ‘‘turbulent’’ self-organizing subsystem including gH, gLE, NEE, and GEP exists with a feedback time scale of 30 min or less and inhabits a feedback loop with P and Rg at time scales from 30 min to 12 h. The P, CF, and Rg variables form a loose subsystem of mixed types, which interact with each other on a time scale of roughly 12 h. W03419 RUDDELL AND KUMAR: ECOHYDROLOGIC PROCESS NETWORKS, 1 W03419 So what is furtherly next ? More thermo-mechanistic and networks ? We cannot deny that our universe is not a chaos; we recognise being, objects that we recall with names. These objects or things are forms, structures provided of a certain stability; fill a certain portion of space and perdure for a certain time …” (R. Thom, Structural stability and morphogenesys,1975) afterRuddelandKumar,2009 R. Rigon
  36. 36. !36 True for life, true for tomorrow hydrology .. though warned at the outset that the subject-matter was a difficult one a …, even though the physicist’s most dreaded weapon, mathematical deduction, would hardly be utilized. The reason for this was not that the subject was simple enough to be explained without mathematics, but rather that it was much too involved to be fully accessible to mathematics What is life ? E. Schroedinger The large and important and very much discussed question is: How can the events in space and time which take place within the spatial boundary of a living organism be accounted for by physics and chemistry? The preliminary answer which this little book will endeavor to expound and establish can be summarized as follows: The obvious inability of present-day physics and chemistry to account for such events is no reason at all for doubting that they can be accounted for by those sciences A programmatic manifesto based on Schroedinger booklet R. Rigon
  37. 37. !37 I do not believe In holistic views not based on a formal and quantitative (in some sense, mathematical, even if of maybe a new mathematics) approach. R. Rigon Certainly we need of a theory of interactions which helps us to simplify complexities and scale up from the but despite the critical role that stomata play, the details of their regulation are still not fully under- stood.84 Ultimately, stomata are largely regulated biologically, and it is through these tiny apertures (or lack thereof if leaves are shed) that vegetation imprints a unique signature on the water cycle. Each stoma is surrounded by a pair of guard cells that are, in turn, in contact with multiple epider- mal cells (Figure 2). Stomata tend to open when guard cells increase their turgor (the sum of water potential and osmotic pressure, see Eq. (4)), while an increase in epidermal cell turgor results in the oppo- site reaction, exerting a hydromechanically negative feedback85–87 (Figure 2(b)). As the guard cell turgor is the sum of osmotic pressure and water potential, stomatal apertures are controlled by both hydraulic and chemical factors88 (Figure 2(c)). Stomata close when water potential in the leaf drops because of a large transpiration flux or low water potential in the upstream xylem conduits.89–91 The hydraulic control acts directly in the reduction of guard cell turgor, while chemical signals are less well quantified.92 However, it is well established that chemical factors are essential for stomata opening in response to light.93–95 Furthermore, chemical compounds, such as ABA, are typically released in response to water stress from the leaves and roots96–98 and contribute to a reduction in the stomatal aperture.99 Release of ABA is an important evolutionary trait as in early plants such as lycophyte and ferns, stomatal closure is purely hydraulically controlled.100 A differential sensitivity of stomata aperture to chemical com- pounds is a likely explanation why certain plants close stomata considerably in response to dehydrata- tion, keeping a fairly constant leaf water potential (commonly referred to as ‘isohydric behavior’), while others tend to keep stomata open to favor carbon assimilation, experiencing larger fluctuations and lower values of the leaf water potential (‘anisohydric behavior’). Models have been presented to mechanistically describe stomatal behavior and reproduce the hydraulic dynamics in the leaf86,101–108 or simply to reproduce functional relations in agreement with observations.80,109,110 Models that represent the exact mechanisms through which stomata respond to 1 0.8 0.6 0.4 0.2 0 0 1 2 Pg (MPa) Palisade mesophyll Spongy mesophyll Epidermal cell Guard cell Atmosphere Cuticle Phloem Xylem Pe = 0 Pe = 1.5 Hydraulic only Hydraulic + chemical Hydromechanical feedback 3 4 5 Relativestomatalaperture 0.25 0.2 0.15 0.1 0.05 0 0 –0.5 –1 –1.5 –2 ψg (MPa) ψm ψe ψg ψa ψg ψi ψx,v gs(molH2O/m2s) (a) (b) (c) FIGURE 2 | A leaf is mostly composed of mesophyll and epidermal cells. The mesophyll is subdivided into palisade and spongy mesophyll. The epidermis secretes a waxy substance called the cuticle to separate the leaf interior from the external atmosphere. Among the epidermal cells, there are pairs of guard cells. Each pair of guard cells forms a pore called stoma. Water and CO2 enter and exit the leaf mostly through the stomata. The vascular network of the plant is composed of xylem (blue) that transports water to the leaf cells and of phloem (red), which transports sugars from the leaf to the rest of the plant. Water that exits the xylem is evaporated in the leaf interior (dashed lines). The terms Ψx,v Ψm, Ψe, Ψg, Ψi, and Ψa are the water potential in the xylem of the leaf vein, mesophyll cell, epidermal cell, guard cell, leaf interior, and atmosphere, respectively. Stomatal aperture responds positively to guard cell turgor pressure (Pg) and negatively to epidermal cell turgor pressure (Pe) (hydromechanical feedback). The conductance of the stomatal aperture (gs) decreases with water potential in the leaf because of a combination of hydraulic and chemical factors. WIREs Water Modeling plant–water interactions To the
  38. 38. !38 ”So, where is the gold medal ?” I.R.I Giving water to people and ecosystem R. Rigon
  39. 39. !39 Getting new generation of students having success MObyGISgettingtheEdison-EnergyPrize R. Rigon ”So, where is the gold medal ?” I.R.I
  40. 40. !40 Entropy 2014, 16 3484 Figure 1. Quantification of the entropy or exergy budgets in the Critical Zone at different spatial scales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etting the fluxes and thermodynamics right at various scales QuijanooandLin,Entropy,2014 R. Rigon ”So, where is the gold medal ?” I.R.I
  41. 41. !41 Nothing can be achieved without good and sound science R. Rigon Science is not a commodity but at the core of our well being
  42. 42. !42 Without them it would not be possible Sandro Marani Andrea Rinaldo Ignacio Rodriguez-Iturbe The Masters R. Rigon
  43. 43. !43 Giacomo Bertoldi Reza Entezarolmahdi Andrea Antonello Silvia Franceschi Fabrizio Zanotti Emanuele Cordano Stefano Endrizzi Silvia Simoni Agee Bushara Matteo Dall’Amico Cristiano Lanni Giuseppe Formetta Fabio Ciervo Wuletawu Abera Marialaura Bancheri Francesco Serafin The Students who actively participated R. Rigon
  44. 44. !44 Find this presentation at Ulrici,2000? Other material at Questions ? R. Rigon
  45. 45. Find what is missing Riccardo Rigon 16 December 2015 JoshSmith
  46. 46. !46 GEOPHYSICALRESEARCHLETTERS,VOL.22,NO. 20,PAGES2757-2760,OCTOBER15,1995 On thespatialorganizationof soilmoisturefields IgnacioRodriguez-Imrbe,GregorK.Vogel,RiccardoRigon• DepartmentofCivilEngineering,TexasA&MUniversity,CollegeStation,Texas Dara Entekhabi DepartmentofCivilandEnvironmentalEngineering,M.I.T., Cambridge,Massachusetts Fabio Castelli Istitutodi Idraulica,Universithdi Pemgia,Pemgia,Italy Andrea Rinaldo, Istitutodi Idraulica"G. Pleni," Universithdi Padova,Padova,Italy Abstract. We examine the apparent disorder which seemsto characterizethe spatial structure of soilmois- ture by analyzinglarge-scaleexperimentaldata. Specif- ically,we addressthe statisticalstructureof soilmois- ture fields under different scales of observation and findß unexpectedresults. The varianceof soil moisturefol- lowsa powerlaw decayasfunctionof the areaat which theprocessis'observed.Thespatialcorrelationremains unchangedwith the scaleof observationand follows a power law decaytypical of scalingprocesses.Soil moisture also showsclear scalingpropertieson its spa- tial clusteringpatterns. A well-definedorganizationof statistical character is found to exist in soil moisture patternslinking a large rangeof scalesthroughwhich the processmanifestsitselfandimpactsotherprocesses. We suggestthat suchscalingpropertiesare crucialto our currentunderstandingand modelingof the dynam- icsof soil moisture in spaceand time. Introduction Many outstandingissuesin earth and atmospheric sciences[Eagleson,1994],suchas sub-gridscalepa- rameterizationofgeneralcirculationmodels[Entekhabi andEagleson,1989;AvissarandPielke,1989],hydro- logicresponseofriverbasins[Eagleson,1978]andland- atmosphere feedbacks[Delworthand Manabe,1988, 1989],hingein the characteristicsof soilmoisturepat- terns in spaceand time. In thispaperweaddressthe apparentdisorder(and the noteworthyimplications)ofthe spatialandtempo- ral structureofsoilmoisture.Indeed,probablythemost 1OnleavefromDipartimentodi !ngegneriaCivilee Ambien- tale, Universirkdi Trento, Trento, Italy Copyright1995bytheAmericanGeophysicalUnion. challengingand fascinatingaspectin the studyof soil moisture is the continuousspectrum of temporal and spatial scales,from centimetersto thousandsof kilo- meters and from minutes to severalmonths, which are embedded one into another. The phenomenain these scalesare not independentbut the structureof the spa- tial andtemporalpatternsareaffectedby very different variables and mechanisms. This paper focuseson spatial scalesof tens of me- ters to hundredsof kilometers with temporal scalesof the order of one day. This scalerange is of great in- terestin hydrologyfrom the point of view of localand regionalwaterbalance,basinresponse,dynamicsofsoil- water-vegetationsystemsandthe translationof locally measuredfluctuations,tolargerscales.The objective is to study the links betweenthe propertiesof the soil moistureprocesswhenobservedat differentscales.The emphasisis on the spatialcharacterof the fluctuations of soil moisture. The temporal aspect is not a struc- tural part of the analysis,it only playsa role in the time variability that the field undergoeswhen its evo- lution is followedthroughoutseveraldays. Description of data The soilmoisturedatausedin thispaper[Jacksonet al., 1993;Allen andNaney,1991;Jackson,1993]have beencollectedby NASA, the US DepartmentofAgricul- ture and severalagenciesduring the socalledWashita '92 Experiment. This was a cooperativeeffort between NASA, USDA, severalother governmentagenciesand universitiesconductedwith the primary goal of gather- ing a time seriesof spatially distributed data focusing on soil moisture and evaporative fluxes. Data collection was conducted from June 10 to June 18, 1992. The regionreceivedheavyrainsovera period beforethe experimentstarted with the rain endingon June 9 and no precipitation occurring during the ex- 5 Soil moisture statistics R. Rigon 2.5 3.0 3.5 Log distance (m) Figure 2. Correlation functionof the relativesoilmois- ture field. The processis describedin 200 m by 200 m pixels and the correlation is estimated at distancesmul- tiple of 200 m. The slopesof the fitting linesare: day 11,-0.33; day 14,-0.35; day 18,-0.48. portance. From the theoretical point of view it opens the door to an unifying- acrossscales- type of analy- (a) showsexamplesof the powerlawsobtainedfor the size distributions of the soil moisture islands. The level is decreasedwhen advancing in time in order to keep an adequate sample size becauseof the drying effect. In all casesthe fitting is excellentwith exponentsin the range0.75 to 0.95. This implies[Mandelbrot,1975]a very rough fractal perimeter for the soil moistureclus- ters. The fractal characteristicsappear to depend on the crossinglevel pointing out the likely multiscaling structure of the field. Other types of clustering patterns were studied for sisofthe spatialshapespresentin soilmoisture.From. theWashitadata. AnexampleofthisisshowninFigure the practical point of view it allows the quantitative probabilisticassessmentof the patchesof differentsoil moisture levels. ' • 0 ..... day14S>0.50 -0.5 .35 -1.5 -2.0 -2.5 .............. , ........ J ...... 5 6 7 Loga (m2) Figure 3. (a) Probabilitydistributionofthesizeofsoil moistureislandsabovedifferentthresholds.The slopes 3 (b). In all casesthe powerlaw fitting is excellent confirmingthe scalingnature of the spatial patterns. We finally notice that the sizeof the area involvedin the Washira '92 experimentmakesit unrealisticthat the scalingdetectedin the soil moisturefieldshasany re- lation with the dynamicsof soil-atmosphereinteraction phenomenaor with any appreciablespace-timeorgani- zation of rainfall. We also observe that from the surface runoffviewpointthereisnot muchredistributionexcept through the channelnetwork which will take lessthan one day to move the water out of the regiononceit reachesthe network.Also,fromthe viewpointofsignif- icant moistureredistributionthroughundergrounddy- namics, the time scalesinvolved make that mechanism ineffectivefor the type of data at hand[Entekhabiand Rodriguez-Iturbe,1994]. Some statistical properties of the porosity field The abovereasoningsuggeststhat the spatialscaling of soil moistureat the scalesof this study is a conse- quenceofthe existenceofspatialorganizationin the soil properties which command the infiltration of moisture.
  47. 47. !47 Potential for landsliding: Dependence on hyetograph characteristics Paolo D’Odorico Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, USA Sergio Fagherazzi Department of Geological Sciences and School of Computational Science and Information Technology, Florida State University, Tallahassee, Florida, USA Riccardo Rigon Dipartimento di Ingegneria Civile e Ambientale, Universita´ di Trento/CUDAM, Trento, Italy Received 27 January 2004; revised 8 November 2004; accepted 14 December 2004; published 10 February 2005. [1] This paper examines the effect of hyetograph shape on the potential for landsliding in soil-mantled landscapes. An existing pore pressure response model (Iverson, 2000) is used to study the effects of unsteady rainfall infiltration in hillslopes, and the effect of slope and convergent topography is expressed using a steady state model of slope-parallel subsurface flow. Slope stability is assessed using an infinite slope analysis. This theoretical framework is coupled with simple hyetograph models and to intensity-duration-frequency functions to determine the return period of landslide-triggering rainfall. Results also show that hyetographs with a peak at the end of a rainfall event have a stronger destabilizing effect than hyetographs with a constant rainfall or with a peak at the beginning of a storm. Thus the variability of hyetograph shapes adds uncertainty to the assessment of landsliding triggered by rainfall. Citation: D’Odorico, P., S. Fagherazzi, and R. Rigon (2005), Potential for landsliding: Dependence on hyetograph characteristics, J. Geophys. Res., 110, F01007, doi:10.1029/2004JF000127. 1. Introduction [2] The stability of hillslopes and hollows is affected by extreme rainfalls which increase the soil water pressure and consequently reduce the shear strength of the aggregates, favoring the instability of the soil mantle. The triggering of landslides by rainfall has been studied on different slopes, bedrock substrates, soil depths and ages, and with different vegetation [O’Loughlin and Pearce, 1976; Sidle and Swanston, 1982; Reneau and Dietrich, 1987; Trustrum and De Rose, 1988; Montgomery et al., 1998; Iida, 1999; Wieczorek et al., 2000; Morrissey et al., 2001]. The process-based assessment of whether a slope is stable or unstable is important to landslide hazard assessment, mapping of landslide-prone areas [Montgomery and Dietrich, 1994; Dietrich et al., 1995; Iida, 1999], and studies of landform evolution [e.g., Benda and Dunne, 1997a, 1997b; D’Odorico and Fagherazzi, 2003], as well as to investigations of the dependence of landslide frequency on soil mechanical properties, land cover, and topography [e.g., Sidle and Swanston, 1982; Sidle, 1987; Iida, 1999]. [3] This paper puts together an existing body of modeling approaches and hydrological concepts in a methodology to calculate the return period of landslide-triggering precipi- duration, and frequency of precipitation (through the IDF curves expressed as power laws [e.g., Koutsoyiannis and Foufoula-Georgiou, 1993; Burlando and Rosso, 1996]); (2) the relative importance of long-term (slope-parallel) flow with respect to short-term (vertical) infiltration in the triggering of landslides; and (3) the influence of the shape of the storm hyetographs on the return period of landslide- triggering precipitation. [4] We assume that the pressure head transient observable in the course of a rainstorm is due to the unsteady vertical flow through the soil profile, while slope-parallel subsurface flow is assumed to occur at a longer timescale and to determine the prestorm wetness conditions. We therefore couple a model of subsurface lateral (steady) flow [Montgomery and Dietrich, 1994; Iida, 1999] with a model of transient rainfall infiltration [Iverson, 2000] to determine the hydro- logic conditions that cause slope failure. The return period of these hydrologic conditions is determined through the intensity-duration-frequency relations of extreme precipita- tion. Moreover, simplified models of the complex temporal structure of storm hyetographs are used here to study the effect of hyetograph shape on slope stability. 2. Spatial and Temporal Variability of Soil Water JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 110, F01007, doi:10.1029/2004JF000127, 20058 R. Rigon HYDROLOGICAL PROCESSES Hydrol. Process. 22, 532–545 (2008) Published online 8 October 2007 in Wiley InterScience ( DOI: 10.1002/hyp.6886 Modelling the probability of occurrence of shallow landslides and channelized debris flows using GEOtop-FS† Silvia Simoni,* Fabrizio Zanotti, Giacomo Bertoldi and Riccardo Rigon Department of Civil and Environmental Engineering, CUDAM, University of Trento, Trento, Italy Abstract: This paper describes a coupled, distributed, hydrological-geotechnical model, GEOtop-FS, which simulates the probability of occurrence of shallow landslides and debris flows. We use a hydrological distributed model, GEOtop, which, models latent and sensible heat fluxes and surface runoff, and computes soil moisture in 3-D by solving Richards’equation numerically, together with an infinite-slope geotechnical model, GEOtop-FS. The combined model allows both the hydraulic and geotechnical properties of soil to be considered and realistically modelled. In particular, the model has been conceived to make direct use of field surveys, geotechnical characteristics and soil moisture measurements. In the model the depth of available sediments is also used to characterize the hydraulic properties of the area examined. To account for the uncertainty related to the natural variability in the factors influencing the stability of natural slopes, the safety factor is computed with a probabilistic approach. In order to determine the likelihood of slope failures, soil parameters are assigned distributions instead of single deterministic values. The analysis presented was carried out for an alpine watershed, located in the Friuli region, Italy, for which some geological and geotechnical data were available. In the past, this watershed experienced landslides and debris flows during intense storms following long and moderate intensity rainfall events. The distributed coupled GEOtop-FS model was calibrated by reproducing some of these events and validated in order to map future failure probabilities. Copyright © 2007 John Wiley & Sons, Ltd. KEY WORDS shallow landslides; failure probability; soil characterization; stability analysis Received 9 June 2006; Accepted 5 October 2006 INTRODUCTION The triggering point of shallow landslides, that eventually turn into debris flows or mudflows in channeled areas, cannot currently be predicted NRC-National Research create interpretative maps that show the potential extent of landslide activity (Glade et al., 2000; Pitman et al., 2003). The products of this analysis are often landslide susceptibility maps (Guzzetti et al., 1999) produced with 542 S. SIMONI ET AL. Figure 8. Variation of the probability of failure, in terms of percentage, with volumetric soil moisture evolution [ ]. Water content rea maximum value at day 90 which corresponds to a higer probability of failure. Grey areas (15% total catchment area) represent expose characterized by a very steep topography, where the slope angle is larger than the internal friction angle, and areas with scarce sediment av layer could drive the stability of the more superficial lay- ers; therefore stability results must be considered from bottom-up, bearing in mind that, for a given pixel, the depth of slipping soil is given by the weakest layer. The stability analysis with respect to time is shown in Figures 10 and 11. For a given depth (layer), they dis- play the variation with time of stable and unstable areas, respectively, highlighting that unstable areas increase 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Area% Landslide triggering
  48. 48. !48 11Carbonate pseudotachylytes: evidence for seismic faulting along carbonate faults Alfio Vigano`,1 Simone Tumiati,2 Sandro Recchia,3 Silvana Martin,4 Marcello Marelli5 and Riccardo Rigon1 volume 23 number 3 june 2011
  49. 49. Information Riccardo Rigon 16 December 2015 JacquelineHumphries,HowthisWorks,2007
  50. 50. !50 me my past Research with papers pdfs my future Research About R. Rigon
  51. 51. !51 About R. Rigon GEOtop JGrass-NewAGE The Horton Machine was in Grass, Jgrass, dig, STAGE and will be in GVSig
  52. 52. !52 About The future of my past research topics (with or without me) is a complex question. I will try to answer in a blog-post, in the first days of January 2016. R. Rigon See: