Downward approach to hydrological prediction

Sivapalan, Murugesu; Zhang, Lu; Vertessy, Rob; Blöschl, Günter

doi:10.1002/hyp.1426

Cited by 129 publications

(172 citation statements)

References 2 publications

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“…While this, understandably, is not an easy thing to do, especially in light of our increasingly available resources, the recent surge in such acknowledgments is indeed encouraging. For example, the studies by Young et al (1996), Grayson and Blöschl (2000), Beven (2002), Woods (2002), Sivapalan et al (2003), Sivakumar (2004b), and Wainwright and Mulligan (2004) advocate in one way or another moving beyond the notion of ''modeling everything'' and adopting the notion of ''capturing the essential features.'' Their argument, in essence, is: ''… we should be developing methods to identify dominant processes that control hydrologic response (in different environments, landscapes and climates, and at different scales) and then developing models to focus on these dominant processes.''…”

Section: Model Simplification and Dominant Processes Conceptmentioning

confidence: 96%

“…There is no question that these issues have already been part of our growing research activities: the ideas of model simplification and collection of more reliable but only relevant data towards a more effective and efficient use of our resources are central in many studies of diverse forms (e.g., Jakeman and Hornberger 1993;Hsu et al 1995;Puente and Obregon 1996;Young et al 1996;Grayson and Blöschl 2000;Sivakumar et al 2001;Beven 2002;Young and Parkinson 2002;Wainwright and Mulligan 2004), and the need for a common framework or similar has been recognized by many researchers (e.g., Dooge 1986;Dunne 1998;Gupta et al 2000;Woods 2002;Sivapalan et al 2003;Gupta 2004;Sivakumar 2004b;Sivapalan 2005;Dawdy 2007;Sivakumar et al 2007). The problem, however, is that efforts to bring these mostly disparate studies together, towards finding common grounds in our research, are almost nonexistent.…”

Section: Introductionmentioning

confidence: 98%

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Dominant processes concept, model simplification and classification framework in catchment hydrology

Sivakumar

2007

Stoch Environ Res Risk Assess

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Technological and methodological advances have facilitated tremendous growth in hydrology during the last century; however, there are also concerns that these advances indirectly contribute to additional problems in our research. An insight into hydrologic literature reveals our tendency to develop more complex models than perhaps needed, and our increasing emphasis on individual mathematical techniques rather than general hydrologic issues. Some recent studies of diverse forms have suggested that simplification in modeling and development of a common framework may help alleviate these problems. The present study is intended to bring such studies together towards a more coherent approach to research in catchment hydrology. This is done by highlighting the need for model simplification and generalization and proposing some potential directions for achieving such. Through a discussion of difficulties in data measurements, the need for moving beyond the notion of ''modeling everything'' to the notion of ''capturing the essential features'' is explained; the concept of dominant processes in model simplification and the utility of integration of concepts for modeling improvement are discussed. Formulation of a catchment classification framework is advocated as a possible means for a common framework in hydrology, and the role of dominant processes in this formulation is presented; the problems due to adoption of different modeling terminologies are highlighted and potential ways to overcome such are also discussed.

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Section: Model Simplification and Dominant Processes Conceptmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 98%

Dominant processes concept, model simplification and classification framework in catchment hydrology

Sivakumar

2007

Stoch Environ Res Risk Assess

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“…Moreover, as in Botter et al [2005], an assessment of spatial effects is needed so as to highlight at what nonpoint-source injection scales (relative to correlation scales of heterogeneous properties) one could assume the processes as chiefly driven only by contact times between fixed and mobile phases. A distinct advantage of SAS function approaches is that transport parameters pertaining to travel times may be decoupled from the ones Sivapalan et al, 2003]. A top-down approach requires application to a large number of systems where appropriate data are available, and eventually a synthesis of the estimated SAS functions to infer patterns.…”

Section: 1002/2015wr017273mentioning

confidence: 99%

“…As experimental evidence has mounted on the mutual links among the hydrochemistry of waters in storage and in fluxes, climatic regimes, soil and landscape attributes, and the age of streamflows [e.g., Weiler and Fl€ uhler, 2004;McGuire et al, 2005;Hrachowitz et al, 2009;Soulsby et al, 2011;Tetzlaff et al, 2014], a growing awareness has developed of the fundamental importance of a unifying theoretical framework for catchment-scale flow and transport phenomena [e.g., Rinaldo and Marani, 1987;Kirchner, 2003;Sivapalan et al, 2003]. Such a framework must formally characterize the age dynamics in hydrologic systems and, as a consequence, comprehensively recapitulate hydrograph and tracer information.…”

Section: Introductionmentioning

confidence: 99%

Storage selection functions: A coherent framework for quantifying how catchments store and release water and solutes

Rinaldo

Benettin

Harman

et al. 2015

Water Resources Research

214

269

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We discuss a recent theoretical approach combining catchment-scale flow and transport processes into a unified framework. The approach is designed to characterize the hydrochemistry of hydrologic systems and to meet the challenges posed by empirical evidence. StorAge Selection functions (SAS) are defined to represent the way catchment storage supplies the outflows with water of different ages, thus regulating the chemical composition of out-fluxes. Biogeochemical processes are also reflected in the evolving residence time distribution and thus in age-selection. Here we make the case for the routine use of SAS functions and look forward to areas where further research is needed.

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“…While high complexity is desirable to simulate a rich class of emergent patterns, such models when calibrated, especially for sparsely gauged basins (in terms either of socio-economic or hydrological data), may not reliably predict the dynamics driven by future yet unseen exogenous forcing. See for example Sivapalan et al (2003), Jakeman and Letcher (2003), Fenicia et al (2008), Pande et al (2012), Pande (2013), and Arkesteijn and Pande (2013) for extensive analyses of the relationships between model complexity, model structure deficiency, prediction uncertainty. Furthermore, the differences in the shapes of the curves between observations and predictions, especially in the case of irrigation area, points to model improvements that can still be made: for example, the assumption that attractiveness level is a function of irrigation potential may have to be improved with the hindsight of additional data.…”

Section: Temporal and Spatial Dynamics Of The State Variables And Fluxesmentioning

confidence: 99%

Socio-hydrologic modeling to understand and mediate the competition for water between agriculture development and environmental health: Murrumbidgee River basin, Australia

Emmerik

Sivapalan

et al. 2014

Hydrol. Earth Syst. Sci.

159

144

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Abstract. Competition for water between humans and ecosystems is set to become a flash point in the coming decades in many parts of the world. An entirely new and comprehensive quantitative framework is needed to establish a holistic understanding of that competition, thereby enabling the development of effective mediation strategies. This paper presents a modeling study centered on the Murrumbidgee River basin (MRB). The MRB has witnessed a unique system dynamics over the last 100 years as a result of interactions between patterns of water management and climate driven hydrological variability. Data analysis has revealed a pendulum swing between agricultural development and restoration of environmental health and ecosystem services over different stages of basin-scale water resource development. A parsimonious, stylized, quasi-distributed coupled socio-hydrologic system model that simulates the twoway coupling between human and hydrological systems of the MRB is used to mimic and explain dominant features of the pendulum swing. The model consists of coupled nonlinear ordinary differential equations that describe the interaction between five state variables that govern the co-evolution: reservoir storage, irrigated area, human population, ecosystem health, and environmental awareness. The model simulations track the propagation of the external climatic and socio-economic drivers through this coupled, complex system to the emergence of the pendulum swing. The model results point to a competition between human "productive" and environmental "restorative" forces that underpin the pendulum swing. Both the forces are endogenous, i.e., generated by the system dynamics in response to external drivers and mediated by humans through technology change and environmental awareness, respectively. Sensitivity analysis carried out with the model further reveals that socio-hydrologic modeling can be used as a tool to explain or gain insight into observed co-evolutionary dynamics of diverse human-water coupled systems. This paper therefore contributes to the ultimate development of a generic modeling framework that can be applied to human-water coupled systems in different climatic and socio-economic settings.

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Downward approach to hydrological prediction

Cited by 129 publications

References 2 publications

Dominant processes concept, model simplification and classification framework in catchment hydrology

Dominant processes concept, model simplification and classification framework in catchment hydrology

Storage selection functions: A coherent framework for quantifying how catchments store and release water and solutes

Socio-hydrologic modeling to understand and mediate the competition for water between agriculture development and environmental health: Murrumbidgee River basin, Australia

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