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OSU GEO 599 - Module 1­ Disturbance propagation in stream networks in space and time

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Geo 599 – Concepts in Ecosystem InformaticsModule 1- Disturbance propagation in stream networks in space and timeJ.A. Jones.Part 1 of 4.1. Key concepts.A) a list of 5 to 10 key terms/concepts that we (faculty and students) should all know, from your disciplines (and be prepared to define them)1-Disturbance, succession, disturbance history of a siteDisturbance – deviation from succession; physical or biological sudden changeSuccession – predictable sequence of (plant) species and (vegetation) structure in timeDisturbance history of a site:- multiple disturbances- multiple types- order is important (Harvard Forest)2-Laws of thermodynamics, pressure gradients, heat/energy transfer, latent heatLaws of thermodynamics: - conservation of matter and energy- entropy (declining free energy)-Pressure gradients – produce motion of air, waterSolar energyHeat/energy transfer- conduction- convection- radiationEnergy transfer through changes of state- latent heat- heat of vaporization (endothermic)- heat of condensation (exothermic)Energy to do work- potential- kinetic- Coriolis forces3-Climate, seasonality, atmospheric and ocean circulationLaws of thermodynamics + changes of state of water + motion of air and water= atmospheric circulation AND ocean conveyor belt AND climate+ rotation, orbit, tilt of Earth and orbital variations= seasons, glacial cycles4-Hydrologic cycle, roles of vegetationhydro cycle = movement of water through- atmosphere- biosphere- geosphereprocesses in hydrologic cycle that are tied to vegetation- interception- throughfall, stemflow- evaporation- transpiration- soil moisture5-Networks: small world, branching hierarchical v. patchworks: adjacency, linear distancesnetworks- arcs, nodes- branching hierarchical- small world- in space, in time (evolution)patchworks- patch size- patch spacing- amount of edge6-Scale, grain, extent, scaling laws, self-similarity, fractals, power lawScale = grain and extentScaling laws- self-similarity- power law- fractals7-Spatial transfers of water, sediment, wood in landscapes; Landform evolutionWater storage and routing- soil moisture, water in veg and atmosphere- floods, flood routingsediment and wood storage and routing- sediment on hillslopes- wood on hillslopes- sediment in streams- wood in streams8-Causality, experiments, observational studies, causal inferenceCausality- cause- effect- attributeExperiment- control- treatment- replicate- temporal stability, causal transience- unit homogeneity9-Empirical models: Statistical significance, type 1 and type 2 errors, statistical powerModel fit to data- goodness of fit- types of error (fail to reject the null, fail to accept the alternative)- selection of functional form, overfittingStatistical power10-Simulation and optimization models: discriminating among multiple solutions (JCA papers; Revelle, Church)Simulation model: equations, variablesOptimization model: objective function equation, constraint equations, variablesInteger vs. continuous optimization (networks vs. patchworks)Selecting among feasible solutions: solution algorithms; heuristicsGeo 599 – Concepts in Ecosystem InformaticsModule 1- Disturbance propagation in stream networks in space and timeJ.A. Jones.Pre-test1. What are the laws of thermodynamics and what sorts of equations do they permit us towrite about physical entities?2. Name several forms of energy or energy transfer and give an example of each that involves water.3. Earth is an open system with respect to _________________, but a closed system withrespect to ___________________ (name some form(s) of energy/matter). Sketch an opensystem and a closed system.4. How do various forms of energy and heat exchanges give rise to atmospheric and ocean circulation?5. Draw the hydrologic cycle. List the components that are linked to vegetation.6. Draw a branching hierarchical network and a small world network. How do they differin form and function?7. How do statistical methods help one to discriminate among alternative empirical models?8. How does optimization help one to discriminate among alternative mathematical models for a given


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