Typology of hydrologic predictability

[1] Prediction problems broadly deal with ascertaining the fate of fluctuations or instabilities through the dynamical system being modeled. Predictability is a measure of our ability to provide knowledge about events that have not yet transpired or phenomena that may be hitherto unobserved or unrecognized. The challenges associated with these two problems, that is, forecasting a future event and identifying a novel phenomenon, are distinctly different. Whereas the prediction of novel phenomena seeks to explore all possible logical space of a model's behavioral response, the prediction of future events seeks to constrain the model response to a specific trajectory of the known history to achieve the least uncertainty for the forecast. Predictability challenges have been categorized as initial value, boundary value, and parameter estimation problems. Here I discuss two additional types of challenges arising from the dynamic changes in the spatial complexity driven by evolving connectivity patterns during an event and cross-scale interactions in time and space. These latter two are critical elements in the context of human and climate-driven changes in the hydrologic cycle as they lead to structural change–induced new connectivity and cross-scale interaction patterns that have no historical precedence. To advance the science of prediction under environmental and human-induced changes, the critical issues lie in developing models that address these challenges and that are supported by suitable observational systems and diagnostic tools to enable adequate detection and attribution of model errors.

[1]  Praveen Kumar,et al.  Ecohydrologic process networks: 1. Identification , 2009 .

[2]  William H. McDowell,et al.  Biogeochemical Hot Spots and Hot Moments at the Interface of Terrestrial and Aquatic Ecosystems , 2003, Ecosystems.

[3]  John Doherty,et al.  A short exploration of structural noise , 2010 .

[4]  Monica G. Turner,et al.  Cross–Scale Interactions and Changing Pattern–Process Relationships: Consequences for System Dynamics , 2007, Ecosystems.

[5]  S. Sorooshian,et al.  Multi-model ensemble hydrologic prediction using Bayesian model averaging , 2007 .

[6]  Michael E. Barrett,et al.  Drainage hydraulics of permeable friction courses , 2008 .

[7]  H. Haken Advanced Synergetics: Instability Hierarchies of Self-Organizing Systems and Devices , 1983 .

[8]  Stephen R Carpenter,et al.  Multiple states in river and lake ecosystems. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[9]  Nigel T. Roulet,et al.  Investigating hydrologic connectivity and its association with threshold change in runoff response in a temperate forested watershed , 2007 .

[10]  Dennis McLaughlin,et al.  An integrated approach to hydrologic data assimilation: interpolation, smoothing, and filtering , 2002 .

[11]  Timothy DelSole,et al.  Predictability and Information Theory. Part I: Measures of Predictability , 2004 .

[12]  Timothy DelSole,et al.  Predictability and Information Theory. Part II: Imperfect Forecasts , 2005 .

[13]  R. Stouffer,et al.  Stationarity Is Dead: Whither Water Management? , 2008, Science.

[14]  Péter Csermely,et al.  Weak links : stabilizers of complex systems from proteins to social networks , 2006 .

[15]  Praveen Kumar,et al.  Kinematic dispersion in stream networks 2. Scale issues and self‐similar network organization , 2002 .

[16]  Stuart N. Lane,et al.  Representation of landscape hydrological connectivity using a topographically driven surface flow index , 2009 .

[17]  Brandon T Bestelmeyer,et al.  Cross-scale interactions, nonlinearities, and forecasting catastrophic events. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[18]  John Harte,et al.  Toward a Synthesis of the Newtonian and Darwinian Worldviews , 2002 .

[19]  R. Betts,et al.  Detection of a direct carbon dioxide effect in continental river runoff records , 2006, Nature.

[20]  E. Lorenz Deterministic nonperiodic flow , 1963 .

[21]  Katerina Michaelides,et al.  Connectivity as a concept for characterising hydrological behaviour , 2009 .

[22]  Dennis McLaughlin,et al.  Recent developments in hydrologic data assimilation , 1995 .

[23]  Praveen Kumar,et al.  Ecohydrologic process networks: 2. Analysis and characterization , 2009 .

[24]  Michael N. Gooseff,et al.  Hydrologic connectivity between landscapes and streams: Transferring reach‐ and plot‐scale understanding to the catchment scale , 2009 .

[25]  Murugesu Sivapalan,et al.  Ecohydrological responses of dense canopies to environmental variability: 1. Interplay between vertical structure and photosynthetic pathway , 2010 .

[26]  George Kuczera,et al.  Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors , 2010 .

[27]  Praveen Kumar,et al.  Variability, Feedback, and Cooperative Process Dynamics: Elements of a Unifying Hydrologic Theory , 2007 .

[28]  Tony Hey,et al.  The Fourth Paradigm , 2009 .

[29]  Praveen Kumar,et al.  A data mining approach for understanding topographic control on climate-induced inter-annual vegetation variability over the United States , 2005 .

[30]  Robert M. May,et al.  Simple mathematical models with very complicated dynamics , 1976, Nature.

[31]  Joël de Rosnay,et al.  The macroscope: A new world scientific system , 1979 .

[32]  S. Griffies,et al.  A Conceptual Framework for Predictability Studies , 1999 .

[33]  Jeffrey J. McDonnell,et al.  Threshold relations in subsurface stormflow: 2. The fill and spill hypothesis , 2006 .

[34]  Yuqiong Liu,et al.  Reconciling theory with observations: elements of a diagnostic approach to model evaluation , 2008 .

[35]  Peter Lehmann,et al.  Hydrology and Earth System Sciences Rainfall Threshold for Hillslope Outflow: an Emergent Property of Flow Pathway Connectivity , 2022 .

[36]  Richard P. Hooper,et al.  Moving beyond heterogeneity and process complexity: A new vision for watershed hydrology , 2007 .

[37]  J. Willems Paradigms and puzzles in the theory of dynamical systems , 1991 .

[38]  S. Attinger,et al.  Importance of spatial structures in advancing hydrological sciences , 2006 .

[39]  D. Lawrence,et al.  Regions of Strong Coupling Between Soil Moisture and Precipitation , 2004, Science.

[40]  L. K. Sherman Streamflow from rainfall by the unit-graph method , 1932 .

[41]  Yuqiong Liu,et al.  Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework , 2007 .

[42]  Michael K. Tippett,et al.  Predictability: Recent insights from information theory , 2007 .

[43]  M. Sivapalan,et al.  Threshold behaviour in hydrological systems as (human) geo-ecosystems: Manifestations, controls, implications , 2009 .

[44]  R. Grayson,et al.  Toward capturing hydrologically significant connectivity in spatial patterns , 2001 .

[45]  Praveen Kumar,et al.  Kinematic dispersion in stream networks 1. Coupling hydraulic and network geometry , 2002 .

[46]  Peter A. Troch,et al.  The future of hydrology: An evolving science for a changing world , 2010 .

[47]  Keith Beven,et al.  Prophecy, reality and uncertainty in distributed hydrological modelling , 1993 .

[48]  Yuichi Onda,et al.  Dynamic runoff connectivity of overland flow on steep forested hillslopes: Scale effects and runoff transfer , 2008 .

[49]  John M. Lewis,et al.  Roots of Ensemble Forecasting , 2005 .

[50]  S. Long,et al.  Food for Thought: Lower-Than-Expected Crop Yield Stimulation with Rising CO2 Concentrations , 2006, Science.

[51]  I. Rodríguez‐Iturbe,et al.  The geomorphologic structure of hydrologic response , 1979 .

[52]  K. Popper,et al.  The Logic of Scientific Discovery , 1960 .

[53]  Hubert H. G. Savenije,et al.  HESS Opinions "The art of hydrology" , 2008 .

[54]  S. Archer,et al.  TREES IN GRASSLANDS: BIOGEOCHEMICAL CONSEQUENCES OF WOODY PLANT EXPANSION , 2001 .

[55]  S. Long,et al.  What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. , 2004, The New phytologist.

[56]  Praveen Kumar,et al.  A model for hydraulic redistribution incorporating coupled soil-root moisture transport , 2007 .

[57]  P. E. O'connell,et al.  IAHS Decade on Predictions in Ungauged Basins (PUB), 2003–2012: Shaping an exciting future for the hydrological sciences , 2003 .

[58]  Murugesu Sivapalan,et al.  Ecohydrological responses of dense canopies to environmental variability: 2. Role of acclimation under elevated CO2 , 2010 .