Title Developing predictive insight into changing water systems : Use-inspired hydrologic science for the anthropocene Permalink

Globally, many different kinds of water resources management issues call for policyand infrastructure-based responses. Yet responsible decision-making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadalto century-long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle – a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges from the perspectives of hydrologic science research. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management. Fully realizing the potential of this approach will ultimately require detailed integration of social science and physical science understanding of water systems, and is a priority for the developing field of sociohydrology. 1 Predictions under change The effect of human activities on the water cycle is deepening and widening rapidly across the planet, driven by increased demands for energy (King and Webber, 2008; Koutsoyiannis et al., 2009), water (Jackson et al., 2001), food (Vörösmarty et al., 2001) and living space (Zhao et al., 2001), and the unintended consequences and secondary effects of land use and Published by Copernicus Publications on behalf of the European Geosciences Union. 5014 S. E. Thompson et al.: Use-inspired hydrologic science for the Anthropocene climate change. Cumulatively, these demands result in increased human appropriation of water resources, significant modification of landscapes, and a strong human imprint on water cycle dynamics from local to global scales (Carpenter et al., 2011; Falkenmark and Lannerstad, 2005; Röckstrom et al., 2009; Vörösmarty et al., 2010). The combination of these effects mean that the world faces a sharp decline in water security (Gleick and Palaniappan, 2010; Postel and Wolf, 2001), which is likely to be most severe in the least resilient of nations (Milly et al., 2002, 2008; Sheffield and Wood, 2008). The increasing human impacts on the water cycle demand effective management, such as the development of infrastructure, policy and law to respond to contemporary problems and create frameworks for future management. Management actions taken today – whether infrastructureor policyrelated – will have long legacies (Swyngedouw, 2009). The lifetimes of artificial reservoirs, for instance, are on the order of 10s to 100s of years (Einsele and Hinderer, 1997). Similarly, the laws governing water rights in the western United States have had decadalto century-scale effects ( ∼ 200 yr for the Prior Appropriation Act,∼ 90 yr for the Law of the River), where incorrect assumptions about flow continue to constrain water management (Garner and Ouellette, 1995; Hundley, 2009; Tarlock, 2002). Thus, the legacies of historical water resource management decisions contribute to contemporary water management problems (Srinivasan et al., 2012). It is likely that humankind will be constrained by water resource availability for the foreseeable future. Contemporary water resources management decisions should therefore attempt to account for their impacts on time horizons commensurate with those of their legacies. These time horizons encompass a period in which we anticipate dramatic changes in climate, population, land uses, and energy and food demand (Huang et al., 2011). Indeed, the human-driven changes in water, nutrient, energy cycles, and landscape evolution may now overwhelm natural variability, leading to the contemporary geologic era being labeled the Anthropocene– the human era (Crutzen and Stoemer, 2000; Poff et al., 2013; Röckstrom et al., 2009; Vitousek et al., 1997; Vörösmarty et al., 2010; Zalasiewicz et al., 2010). To make good decisions about water management today requires a drastic improvement in our ability to predict the dynamics of water resources on long timescales, in the presence of rapid change in multiple elements of the water system, and subject to the direct and indirect influence of human activity (Milly et al., 2008; Wagener et al., 2010). To continue to make good water management decisions as projected changes impact water systems behavior also requires detecting and attributing emergent changes (Maurer et al., 2007), making predictions about their effects on hydrology, and altering management decisions accordingly. The complexity of these issues means that we have taken a broad view of the term “prediction”. At one extreme, we recognize that traditional, deterministic forecasts are likely to be impossible for complex systems containing human agents, particularly on long timescales. At the other extreme, we disagree with the assertion that deep uncertainty would render improved understanding, modeling and predictive assessments meaningless. Instead, we suggest a middle path that asserts that the combination of specific initial and boundary conditions and process interactions among physical, socio-cultural and ecological domains, will constrain the possible future trajectories of water systems, rendering some outcomes more (or less) likely. Identification of the critical initial and boundary conditions and interacting processes is a non-trivial task that itself requires a significant research focus (see Sect. 3). Assuming the problem can be suitably defined, and depending on the timescale of the prediction, which affects the development of uncertainty, such constraints may provide a basis for visualization, understanding and intervention, and for the formulation of a constrained range of potential future scenarios for analysis. Indeed, the lead-time of the prediction is the fundamental driver of the interactions between model structure, prediction goals and increase of uncertainty. We refer to these modest goals as the development of predictive insights, which include predictions of a phenomenological or qualitative nature (Kumar, 2011). The hydrological prediction frameworks that are widely applied for managing water resources today derive from a reductionist paradigm that attempts to upscale microscopic process knowledge to large spatial and temporal scales (Wagener et al., 2010), and may not be well aligned towards developing predictive insight into complex systems. Several commentators have already called for new ways to do water science that are based on exploration of patterns, macroscopic or “top-down” hydrologic prediction and comparative approaches (Blöschl, 2006; McDonnell et al., 2007; Sivapalan et al., 2003), with the intention that such techniques could support hydrologic predictions in the Anthropocene (Killeen and Abrajano, 2008; Wagener et al., 2010; Reed and Kasprzyk, 2009). Such a research approach, however, raises pragmatic questions. For instance, the respective roles of single-investigator research approaches versus community-wide “big science” endeavors in this arena must be better defined, since team science approaches may be better suited to synthesis research (Blöschl, 2006; Torgersen, 2006). Use-inspired hydrologic science must also be careful to avoid devaluing “pure science” approaches to hydrology (Dunne et al., 1998). Thus, developing predictive insight to support water management in the Anthropocene not only poses fundamental scientific challenges, but also non-trivial practical challenges for the water science community. As a response to these issues, we convened a series of workshops for the hydrologic community in 2009–2010 to discuss the grand challenge of making hydrological predictions in the Anthropocene (Sivapalan, 2011). This article represents a distillation of the ideas generated from this large, grassroots effort. Here we firstly identify core impediments to hydrologic prediction in the Anthropocene and argue that Hydrol. Earth Syst. Sci., 17, 5013–5039, 2013 www.hydrol-earth-syst-sci.net/17/5013/2013/ S. E. Thompson et al.: Use-inspired hydrologic science for the Anthropocene 5015 there are tangible research methods available to the hydrologic research community that can begin to address these problems. Secondly, we propose that a “use-inspired” approach towards the planning and execution of this research (Stokes, 1997) provides a way to simultaneously advance fundamental knowledge and its applicability to water resources management, and thus navigating some of the tensions that arise between doing science to expand fundamental knowledge, and doing science to improve human and environmental well-being. The agenda outlined here aspires to improve the capacity of hydrologic researchers to meet the prediction needs posed by water resources management challenges. It is focused, however, on addressing gaps that can be identified within the current portfolio of physical hydrologic science,

[1]  Murugesu Sivapalan,et al.  Comparative hydrology across AmeriFlux sites: The variable roles of climate, vegetation, and groundwater , 2011 .

[2]  Quanxi Shao,et al.  Water balance modeling over variable time scales based on the Budyko framework – Model development and testing , 2008 .

[3]  B. Bobée,et al.  Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks , 2001 .

[4]  Kara L. Hall,et al.  The science of team science: overview of the field and introduction to the supplement. , 2008, American journal of preventive medicine.

[5]  T. D. Mitchell,et al.  Ecosystem Service Supply and Vulnerability to Global Change in Europe , 2005, Science.

[6]  D. Merritts,et al.  Natural Streams and the Legacy of Water-Powered Mills , 2008, Science.

[7]  Graham Jewitt,et al.  Can Integrated Water Resources Management sustain the provision of ecosystem goods and services , 2002 .

[8]  Athol D. Abrahams,et al.  Drainage density in relation to precipitation intensity in the U.S.A. , 1984 .

[9]  D B McGuire Building and maintaining an interdisciplinary research team. , 1999, Alzheimer disease and associated disorders.

[10]  Patrick M. Reed,et al.  Save now, pay later? Multi-period many-objective groundwater monitoring design given systematic model errors and uncertainty , 2011 .

[11]  Joseph R. Kasprzyk,et al.  Water Resources Management: The Myth, the Wicked, and the Future , 2009 .

[12]  J. M. Grove,et al.  Integrating Social Science into the Long-Term Ecological Research (LTER) Network: Social Dimensions of Ecological Change and Ecological Dimensions of Social Change , 2004, Ecosystems.

[13]  Peter A. Troch,et al.  Decreased streamflow in semi-arid basins following drought-induced tree die-off: A counter-intuitive and indirect climate impact on hydrology , 2011 .

[14]  James C. I. Dooge,et al.  Hydrology in perspective , 1988 .

[15]  James C. I. Dooge,et al.  Looking for hydrologic laws , 1986 .

[16]  A. B. Rose,et al.  Vegetation change over 25 years in a New Zealand short-tussock grassland: effects of sheep grazing and exotic invasions. , 1995 .

[17]  Michael N. Gooseff,et al.  Hillslope hydrologic connectivity controls riparian groundwater turnover: Implications of catchment structure for riparian buffering and stream water sources , 2010 .

[18]  F. Chapin,et al.  Planetary boundaries: Exploring the safe operating space for humanity , 2009 .

[19]  Dubravko Justic,et al.  Gulf of Mexico hypoxia: alternate states and a legacy. , 2008, Environmental science & technology.

[20]  Murari Lal,et al.  Implications of climate change in sustained agricultural productivity in South Asia , 2011 .

[21]  Michael L. Pace,et al.  Virtual water transfers unlikely to redress inequality in global water use , 2011 .

[22]  Stefania Tamea,et al.  Verification tools for probabilistic forecasts of continuous hydrological variables , 2006 .

[23]  M. Sivapalan,et al.  Catchment classification: hydrological analysis of catchment behavior through process-based modeling along a climate gradient , 2011 .

[24]  Rudolf Brázdil,et al.  Hydrometeorological extremes derived from taxation records for south-eastern Moravia, Czech Republic, 1751-1900 AD , 2012 .

[25]  Matteo Detto,et al.  Gross ecosystem photosynthesis causes a diurnal pattern in methane emission from rice , 2012 .

[26]  M. Sivapalan,et al.  Catchment classification: Empirical analysis of hydrologic similarity based on catchment function in the eastern USA , 2011 .

[27]  G. Likens,et al.  Technical Report: Human Alteration of the Global Nitrogen Cycle: Sources and Consequences , 1997 .

[28]  Luca Ridolfi,et al.  A Probabilistic Analysis of Fire‐Induced Tree‐Grass Coexistence in Savannas , 2006, The American Naturalist.

[29]  David F. Channell Pasteur's Quadrant: Basic Science and Technological Innovation , 1999 .

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

[31]  P. Crutzen,et al.  The new world of the Anthropocene. , 2010, Environmental science & technology.

[32]  E. G. Bekele,et al.  Multiobjective management of ecosystem services by integrative watershed modeling and evolutionary algorithms , 2005 .

[33]  Luca Ridolfi,et al.  Does globalization of water reduce societal resilience to drought? , 2010 .

[34]  Johan Bouma,et al.  Advances in Hydropedology , 2005 .

[35]  Michael E Webber,et al.  The water intensity of the plugged-in automotive economy. , 2008, Environmental science & technology.

[36]  C. Field,et al.  The velocity of climate change , 2009, Nature.

[37]  Sauleh Siddiqui,et al.  Anthropocene streams and base-level controls from historic dams in the unglaciated mid-Atlantic region, USA , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[38]  Peter C. Young,et al.  Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems , 2013 .

[39]  C. Luce Runoff Prediction in Ungauged Basins: Synthesis Across Processes, Places and Scales , 2014 .

[40]  Robert J. Abrahart,et al.  Hydroinformatics: computational intelligence and technological developments in water science applications—Editorial , 2007 .

[41]  Eric Servat,et al.  FRIEND 2002-Regional Hydrology: Bridging the Gap between Research and Practice , 2002 .

[42]  S. Seneviratne,et al.  Investigating soil moisture-climate interactions in a changing climate: A review , 2010 .

[43]  S. Carpenter,et al.  Early-warning signals for critical transitions , 2009, Nature.

[44]  David J. Earl,et al.  Evolvability is a selectable trait. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[45]  P. Micklin Desiccation of the Aral Sea: A Water Management Disaster in the Soviet Union , 1988, Science.

[46]  C. Rasmussen,et al.  Thermodynamic constraints on effective energy and mass transfer and catchment function , 2011 .

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

[48]  Stefan Rahmstorf,et al.  Simulation of modern and glacial climates with a coupled global model of intermediate complexity , 1998, Nature.

[49]  Soroosh Sorooshian,et al.  Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops , 2006 .

[50]  David L. Hall,et al.  Test and evaluation of soft/hard information fusion systems: A test environment, methodology and initial data sets , 2010, 2010 13th International Conference on Information Fusion.

[51]  Thorsten Wagener,et al.  Convergence of approaches toward reducing uncertainty in predictions in ungauged basins , 2011 .

[52]  J. D. Wulfhorst,et al.  Bridges and Barriers to Developing and Conducting Interdisciplinary Graduate-Student Team Research , 2007 .

[53]  N. Hundley,et al.  Water and the West: The Colorado River Compact and the Politics of Water in the American West , 1976 .

[54]  David B Lindenmayer,et al.  Adaptive monitoring: a new paradigm for long-term research and monitoring. , 2009, Trends in ecology & evolution.

[55]  Sally Thompson,et al.  Plant Propagation Fronts and Wind Dispersal: An Analytical Model to Upscale from Seconds to Decades Using Superstatistics , 2008, American Naturalist.

[56]  J. Lean,et al.  Reconstruction of solar irradiance since 1610: Implications for climate change , 1995 .

[57]  N. Ramankutty,et al.  Green surprise? How terrestrial ecosystems could affect earth’s climate , 2003 .

[58]  Susan L. Brantley,et al.  Frontiers in exploration of the critical zone , 2005 .

[59]  Mei Zhao,et al.  The impact of land cover change on the atmospheric circulation , 2001 .

[60]  M. Hipsey,et al.  “Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022 , 2013 .

[61]  K. Beven Searching for the Holy Grail of Scientific Hydrology: Qt= H(S?R?)A as closure , 2006 .

[62]  A. Dan Tarlock,et al.  The Future of Prior Appropriation in the West , 2002 .

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

[64]  Notes and Comments Self-Organization of Vegetation in Arid Ecosystems , 2002 .

[65]  R Quian Quiroga,et al.  Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[66]  Douglas A. Miller,et al.  Bridging river basin scales and processes to assess human‐climate impacts and the terrestrial hydrologic system , 2006 .

[67]  Jeroen P. van der Sluijs,et al.  A framework for dealing with uncertainty due to model structure error , 2004 .

[68]  P. Coulibaly,et al.  Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting , 2012 .

[69]  Sheila M. Olmstead,et al.  Water Demand Under Alternative Price Structures , 2007 .

[70]  G. Farquhar,et al.  Effects of rising temperatures and [CO2] on the physiology of tropical forest trees , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[71]  Thomas Meixner,et al.  Coevolution of nonlinear trends in vegetation, soils, and topography with elevation and slope aspect: A case study in the sky islands of southern Arizona , 2013 .

[72]  Patrick M. Reed,et al.  Many‐objective groundwater monitoring network design using bias‐aware ensemble Kalman filtering, evolutionary optimization, and visual analytics , 2011 .

[73]  N. Mahowald,et al.  Global review and synthesis of trends in observed terrestrial near-surface wind speeds; implications for evaporation , 2012 .

[74]  Teofilo A. Abrajano,et al.  Understanding the Triple Point , 2008 .

[75]  Johan van de Koppel,et al.  Scale‐Dependent Feedback and Regular Spatial Patterns in Young Mussel Beds , 2005, The American Naturalist.

[76]  G. Di Baldassarre,et al.  Towards understanding the dynamic behaviour of floodplains as human-water systems , 2013 .

[77]  Rob Lamb,et al.  Regional climate‐model predictions of extreme rainfall for a changing climate , 2003 .

[78]  N. Goldenfeld,et al.  Life is Physics: Evolution as a Collective Phenomenon Far From Equilibrium , 2010, 1011.4125.

[79]  Günter Blöschl,et al.  Climate change impacts—throwing the dice? , 2009 .

[80]  William R. Freudenburg,et al.  From LTER to LTSER: Conceptualizing the Socioeconomic Dimension of Long-term Socioecological Research , 2006 .

[81]  Demetris Koutsoyiannis,et al.  HESS Opinions: "Climate, hydrology, energy, water: recognizing uncertainty and seeking sustainability" , 2008 .

[82]  J. L. Bella,et al.  Evaluation of Northern Hemisphere natural climate variability in multiple temperature reconstructions and global climate model simulations , 2001 .

[83]  Peter A. Troch,et al.  An open system framework for integrating critical zone structure and function , 2011 .

[84]  Michael T. Coe,et al.  Reconstructing paleo-precipitation amounts using a terrestrial hydrologic model: Lake Titicaca and the Salar de Uyuni, Peru and Bolivia , 2010 .

[85]  S. Carpenter,et al.  Catastrophic shifts in ecosystems , 2001, Nature.

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

[87]  Scott Rozelle,et al.  The nature and causes of the global water crisis: Syndromes from a meta‐analysis of coupled human‐water studies , 2012 .

[88]  Malin Falkenmark,et al.  Comparative Hydrology: An Ecological Approach to Land and Water Resources , 1989 .

[89]  Sharachchandra Lele,et al.  Climate vulnerability and adaptation of water provisioning in developing countries: approaches to disciplinary and research-practice integration , 2013 .

[90]  Murugesu Sivapalan,et al.  Downward approach to hydrological prediction , 2003 .

[91]  David L. Feldman,et al.  Preventing the repetition: Or, what Los Angeles' experience in water management can teach Atlanta about urban water disputes , 2009 .

[92]  P. McIntyre,et al.  Global threats to human water security and river biodiversity , 2010, Nature.

[93]  Peter A. Troch,et al.  Climate and vegetation water use efficiency at catchment scales , 2009 .

[94]  Demetris Koutsoyiannis,et al.  Climatic Variability Over Time Scales Spanning Nine Orders of Magnitude: Connecting Milankovitch Cycles with Hurst–Kolmogorov Dynamics , 2013, Surveys in Geophysics.

[95]  Lawrence E. Band,et al.  Land Use and Climate Variability Amplify Contaminant Pulses , 2010 .

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

[97]  G. Blöschl,et al.  Socio‐hydrology: A new science of people and water , 2012 .

[98]  Matteo Detto,et al.  Causality and Persistence in Ecological Systems: A Nonparametric Spectral Granger Causality Approach , 2012, The American Naturalist.

[99]  H. Nepf Hydrodynamics of vegetated channels , 2012 .

[100]  Richard M. Vogel,et al.  Flow‐Duration Curves. I: New Interpretation and Confidence Intervals , 1994 .

[101]  R. Villalba,et al.  Climate of the Past Multi-century tree-ring based reconstruction of the Neuqúen River streamflow , northern Patagonia , Argentina , 2012 .

[102]  Praveen Kumar,et al.  Typology of hydrologic predictability , 2011 .

[103]  Keith Beven,et al.  Uniqueness of place and process representations in hydrological modelling , 2000 .

[104]  M. Monirul Qader Mirza,et al.  Climate change and water resources in South Asia , 2005 .

[105]  Ciaran J. Harman,et al.  Vegetation-infiltration relationships across climatic and soil type gradients , 2010 .

[106]  David E. Rosenberg,et al.  Hydro-economic models: concepts, design, applications, and future prospects. , 2009 .

[107]  B. Steinman,et al.  The isotopic and hydrologic response of small, closed‐basin lakes to climate forcing from predictive models: Application to paleoclimate studies in the upper Columbia River basin , 2010 .

[108]  G. Tucker,et al.  Drainage basin responses to climate change , 1997 .

[109]  Ulli Wolff,et al.  Critical slowing down , 1990 .

[110]  C. Pahl-Wostl,et al.  Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/casp.774 Processes of Social Learning in Integrated Resources Management , 2022 .

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

[112]  Keith Smettem,et al.  Welcome address for the new ‘Ecohydrology’ Journal , 2007 .

[113]  Dara Entekhabi,et al.  Impact of Hillslope-Scale Organization of Topography, Soil Moisture, Soil Temperature, and Vegetation on Modeling Surface Microwave Radiation Emission , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[114]  Shlomo P. Neuman Accounting for conceptual model uncertainty via maximum likelihood Bayesian model averaging , 2002 .

[115]  Claudia Pahl-Wostl,et al.  Social Learning in Public Participation in River Basin Management - Early findings from HarmoniCOP European Case Studies , 2005 .

[116]  Richard M. Vogel,et al.  Flow duration curves II : a review of applications in water resources planning , 1995 .

[117]  C. Baeteman,et al.  A Middle to Late Holocene avulsion history of the Euphrates river: a case study from Tell ed-Dēr, Iraq, Lower Mesopotamia , 2008 .

[118]  J. Wheaton,et al.  Preface: Multiscale feedbacks in ecogeomorphology , 2011 .

[119]  Michael E. Webber,et al.  The water intensity of the plugged-in automotive economy (Environmental Science and Technology (2008) 42 (4305-4311)) , 2008 .

[120]  R. Walter,et al.  Mills Natural Streams and the Legacy of Water-Powered , 2008 .

[121]  S. Daniels,et al.  Working Through Environmental Conflict: The Collaborative Learning Approach , 2001 .

[122]  W. B. Langbein,et al.  Overview of conference on hydrologic data networks , 1979 .

[123]  Jeffery S. Horsburgh,et al.  A first approach to web services for the National Water Information System , 2008, Environ. Model. Softw..

[124]  A. Brath,et al.  Analysis of the effects of levee heightening on flood propagation: example of the River Po, Italy , 2009 .

[125]  A. G. Brown An Integrated 1500 Year Record for the River Trent (UK) Using Geomorphological and Geoarchaeological Data , 2009 .

[126]  D. Moorhead,et al.  Increasing risk of great floods in a changing climate , 2002, Nature.

[127]  Yacov Y. Haimes,et al.  The worth of streamflow data in water resources planning: Computational results , 1979 .

[128]  A. Rinaldo,et al.  Structure and controls of the global virtual water trade network , 2011, 1207.2306.

[129]  J. Wayland Eheart,et al.  Reservoir management to balance ecosystem and human needs: Incorporating the paradigm of the ecological flow regime , 2006 .

[130]  Demetris Koutsoyiannis,et al.  Nonstationarity versus scaling in hydrology , 2006 .

[131]  Steven M. Gorelick,et al.  Sustainable urban water supply in south India: Desalination, efficiency improvement, or rainwater harvesting? , 2010 .

[132]  V. Baker,et al.  Paleohydrology of Late Pleistocene Superflooding, Altay Mountains, Siberia , 1993, Science.

[133]  Marshall E. Moss,et al.  Space, time, and the third dimension (model error) , 1979 .

[134]  Alberto Montanari,et al.  Uncertainty of Hydrological Predictions , 2011 .

[135]  Jeffrey L. Anderson,et al.  The Data Assimilation Research Testbed: A Community Facility , 2009 .

[136]  Jeffrey J. McDonnell,et al.  HELPing FRIENDs in PUBs: charting a course for synergies within international water research programmes in gauged and ungauged basins , 2006 .

[137]  Alexander M. Millkey The Black Swan: The Impact of the Highly Improbable , 2009 .

[138]  Julia C. Hargreaves,et al.  Are paleoclimate model ensembles consistent with the MARGO data synthesis , 2011 .

[139]  C. Duffy,et al.  A semidiscrete finite volume formulation for multiprocess watershed simulation , 2007 .

[140]  Stephen R. Carpenter,et al.  State of the world's freshwater ecosystems: physical, chemical, and biological changes. , 2011 .

[141]  Lu Zhang,et al.  Response of mean annual evapotranspiration to vegetation changes at catchment scale , 2001 .

[142]  J. Brander,et al.  The Simple Economics of Easter Island: A Ricardo-Malthus Model of Renewable Resource Use , 1998 .

[143]  Raphael Neukom,et al.  Multi-century tree-ring based reconstruction of the Neuquén River streamflow, northern Patagonia, Argentina , 2011 .

[144]  M. Harmon,et al.  Ecological Variability in Space and Time: Insights Gained from the US LTER Program , 2003 .

[145]  Nicole M. Gasparini,et al.  The Channel-Hillslope Integrated Landscape Development Model (CHILD) , 2001 .

[146]  C. Pahl-Wostl,et al.  Social Learning and Water Resources Management , 2007 .

[147]  H. Madsen,et al.  Bias aware Kalman filters: Comparison and improvements , 2006 .

[148]  V. Klemeš The Hurst Phenomenon: A puzzle? , 1974 .

[149]  E. Wood,et al.  Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations , 2008 .

[150]  Erik Swyngedouw,et al.  The Political Economy and Political Ecology of the Hydro-Social Cycle , 2009 .

[151]  Christopher J. Duffy,et al.  The Hydroarchaeological Method: A Case Study at the Maya Site of Palenque , 2012, Latin American Antiquity.

[152]  C. Vörösmarty,et al.  Global water resources: vulnerability from climate change and population growth. , 2000, Science.

[153]  Kara L Hall,et al.  Moving the science of team science forward: collaboration and creativity. , 2008, American journal of preventive medicine.

[154]  Marten Scheffer,et al.  Slowing down as an early warning signal for abrupt climate change , 2008, Proceedings of the National Academy of Sciences of the United States of America.

[155]  Robert J Lempert,et al.  A new decision sciences for complex systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[156]  M. Hipsey,et al.  Challenges for water-quality research in the new IAHS decade on: Hydrology Under Societal and Environmental Change , 2013 .

[157]  James L. Wescoat,et al.  Reconstructing the duty of water: a study of emergent norms in socio-hydrology , 2013 .

[158]  Keith Beven,et al.  A manifesto for the equifinality thesis , 2006 .

[159]  R. Kates,et al.  Reconstruction of New Orleans after Hurricane Katrina: A research perspective , 2006, Proceedings of the National Academy of Sciences.

[160]  D. M. Sonechkin,et al.  Tree ring-based annual streamflow reconstruction for the Heihe River in arid northwestern China from ad 575 and its implications for water resource management , 2012 .

[161]  Gregory S. Okin,et al.  Impact of feedbacks on Chihuahuan desert grasslands: Transience and metastability , 2009 .

[162]  N. Stephenson Climatic Control of Vegetation Distribution: The Role of the Water Balance , 1990, The American Naturalist.

[163]  Efi Foufoula-Georgiou,et al.  Toward a unified science of the Earth's surface: Opportunities for synthesis among hydrology, geomorphology, geochemistry, and ecology , 2006 .

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

[165]  David L. Strayer,et al.  Climate Change and Freshwater Fauna Extinction Risk , 2012 .

[166]  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.

[167]  Thorsten Wagener Can we model the hydrological impacts of environmental change? , 2007 .

[168]  M. Rietkerk,et al.  Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems , 2007, Nature.

[169]  Charlene D'Avanzo,et al.  A National Ecological Network for Research and Education , 2009, Science.

[170]  Scott Rutherford,et al.  Climate reconstruction using ‘Pseudoproxies’ , 2001 .

[171]  M. Falkenmark,et al.  Consumptive water use to feed humanity - curing a blind spot , 2004 .

[172]  S. Lane Acting, predicting and intervening in a socio-hydrological world , 2013 .

[173]  Tim R. McVicar,et al.  Less bluster ahead? Ecohydrological implications of global trends of terrestrial near‐surface wind speeds , 2012 .

[174]  Robert J. Lempert,et al.  Adapting to Climate Change: Climate prediction: a limit to adaptation? , 2009 .

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

[176]  William W. Hargrove,et al.  NEON: a hierarchically designed national ecological network , 2007 .

[177]  Philip B. Duffy,et al.  Detection, attribution, and sensitivity of trends toward earlier streamflow in the Sierra Nevada , 2007 .

[178]  Amber Wutich,et al.  Hard paths, soft paths or no paths? Cross-cultural perceptions of water solutions , 2013 .

[179]  Paul N. Edwards,et al.  History of climate modeling , 2011 .

[180]  Carrie Morrill,et al.  Changes in the Global Hydrological Cycle: Lessons from Modeling Lake Levels at the Last Glacial Maximum , 2011 .

[181]  Demetris Koutsoyiannis,et al.  HESS Opinions "A random walk on water" , 2009 .

[182]  Marion W. Jenkins,et al.  Climate Warming and Water Management Adaptation for California , 2006 .

[183]  N. Stephenson,et al.  Actual evapotranspiration and deficit: biologically meaningful correlates of vegetation distribution across spatial scales , 1998 .

[184]  Alberto Montanari,et al.  Hydrology of the Po River: looking for changing patterns in river discharge , 2012 .

[185]  M. Bruce Beck Grand challenges for environmental modeling , 2010, Environ. Model. Softw..

[186]  Lucien Duckstein,et al.  The worth of hydrologic data for nonoptimal decision making , 1979 .

[187]  I. Rodríguez‐Iturbe Ecohydrology: A hydrologic perspective of climate‐soil‐vegetation dynamies , 2000 .

[188]  Demetris Koutsoyiannis,et al.  A blueprint for process‐based modeling of uncertain hydrological systems , 2012 .

[189]  Günter Blöschl,et al.  Hydrologic synthesis: Across processes, places, and scales , 2006 .

[190]  Vincent C Tidwell,et al.  Cooperative modeling: linking science, communication, and ground water planning. , 2008, Ground water.

[191]  Weimao Ke,et al.  Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams , 2005, Complex..

[192]  Fubao Sun,et al.  Changes in the variability of global land precipitation , 2012 .

[193]  Thomas Torgersen Observatories, think tanks, and community models in the hydrologic and environmental sciences : How does it affect me? , 2006 .

[194]  Paulin Coulibaly,et al.  Developments in hydrometric network design: A review , 2009 .

[195]  Peter Arzberger,et al.  New Eyes on the World: Advanced Sensors for Ecology , 2009 .

[196]  Sara S. Metcalf,et al.  Sharing the floodplain: Mediated modeling for environmental management , 2010, Environ. Model. Softw..

[197]  Lynn Wu,et al.  Similarity of climate control on base flow and perennial stream density in the Budyko framework , 2013 .

[198]  Rita P. Wright,et al.  Landscapes, soils, and mound histories of the Upper Indus Valley, Pakistan: new insights on the Holocene environments near ancient Harappa , 2004 .

[199]  E. Salas,et al.  Facilitating Innovation in Diverse Science Teams Through Integrative Capacity , 2012 .

[200]  W. Oechel,et al.  FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .

[201]  Joseph R. Kasprzyk,et al.  Many objective robust decision making for complex environmental systems undergoing change , 2012, Environ. Model. Softw..

[202]  Amilcare Porporato,et al.  Causality across rainfall time scales revealed by continuous wavelet transforms , 2010 .

[203]  M. Moss Some basic considerations in the design of hydrologic data networks , 1979 .

[204]  Steven M. Gorelick,et al.  A hydrologic‐economic modeling approach for analysis of urban water supply dynamics in Chennai, India , 2010 .

[205]  Demetris Koutsoyiannis,et al.  Estimating the Uncertainty of Hydrological Predictions through Data-Driven Resampling Techniques , 2015 .

[206]  Connie A. Woodhouse,et al.  Tree rings and multiseason drought variability in the lower Rio Grande Basin, USA , 2013 .

[207]  Richard George,et al.  Dryland salinity in south-western Australia: its origins, remedies, and future research directions , 2002 .

[208]  Ellis Q. Margolis,et al.  A tree-ring reconstruction of streamflow in the Santa Fe River, New Mexico , 2011 .

[209]  Julian D. Olden,et al.  A framework for hydrologic classification with a review of methodologies and applications in ecohydrology , 2012 .

[210]  Avi Ostfeld,et al.  Data-driven modelling: some past experiences and new approaches , 2008 .

[211]  Paul C. Hanson A grassroots approach to sensor and science networks , 2007 .

[212]  E. Ostrom A General Framework for Analyzing Sustainability of Social-Ecological Systems , 2009, Science.

[213]  Murugesu Sivapalan,et al.  Patterns, puzzles and people: implementing hydrologic synthesis , 2011 .

[214]  Sharon E. Nicholson,et al.  The Methodology of Historical Climate Reconstruction and its Application to Africa , 1979, The Journal of African History.