Ecohydrological Optimality

Schymanski, Stanislaus J.; Kleidon, Axel; Roderick, Michael L.

doi:10.1002/0470848944.hsa319

Cited by 11 publications

(5 citation statements)

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“…2007 ; Frank 2013 ; Pruitt and Goodnight 2014 ). A discussion of the consistency of optimality hypotheses with ecological theories is beyond the scope of this paper, but an overview of different optimality approaches relevant to ecohydrology can be found in Schymanski et al . (2009 a ) .…”

Section: Discussionmentioning

confidence: 99%

Using an optimality model to understand medium and long-term responses of vegetation water use to elevated atmospheric CO₂concentrations

Schymanski¹,

Roderick²,

Sivapalan³

2015

AoB PLANTS

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122

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Modelling results presented in this paper suggest that elevated atmospheric CO2 concentrations (eCO2) lead to reductions in leaf-level stomatal conductance and increases in water use efficiency. One consequence was a predicted increase in the perennial vegetation cover and rooting depth in water-limited regions, which provides further support to the view that the globally observed advancement of woody plants is enhanced by fossil fuel emissions. Simulated vegetation responses to eCO2 depend on time scale and climate, but generally compare well with free air CO2 enrichment experiments (FACE).

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Section: Discussionmentioning

confidence: 99%

Using an optimality model to understand medium and long-term responses of vegetation water use to elevated atmospheric CO₂concentrations

Schymanski¹,

Roderick²,

Sivapalan³

2015

AoB PLANTS

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122

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“…In contrast to predicting large-scale behavior emerging from small-scale processes, optimality seeks to predict emergent behavior stemming from self-organization following a macroscopic extremum principle, such as maximization of net carbon profit for vegetation or maximization of energy dissipation or entropy production for both physical and biological systems. The appeal of such extremum principles is that they reduce the number of unknowns in a system, which facilitates generalization, testing, and falsification [Schymanski et al, 2009b;Schaefli et al, 2011]. At the same time, optimality theory can inspire new questions about underlying processes.…”

Section: Optimality-emergent Behaviormentioning

confidence: 99%

Improving the theoretical underpinnings of process‐based hydrologic models

Clark

Schaefli

Schymanski

et al. 2016

Water Resources Research

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111

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In this Commentary, we argue that it is possible to improve the physical realism of hydrologic models by making better use of existing hydrologic theory. We address the following questions: (1) what are some key elements of current hydrologic theory; (2) how can those elements best be incorporated where they may be missing in current models; and (3) how can we evaluate competing hydrologic theories across scales and locations? We propose that hydrologic science would benefit from a model-based community synthesis effort to reframe, integrate, and evaluate different explanations of hydrologic behavior, and provide a controlled avenue to find where understanding falls short. MotivationThe discipline of hydrology continues to be an exciting field, with ongoing advances in field observational techniques, availability of global data products, and increasing computational power. Now, perhaps more than ever before, we are rising to the challenge of building models of everywhere [Beven, 2007]. Key efforts include building continental-domain hydrologic models for water security assessments [Schewe et al., 2014;Mizukami et al., 2015] and improving the representation of hydrologic processes in Earth System Models [Clark et al., 2015a]. These efforts require moving beyond the traditional tactics used in hydrology, such as detailed analysis and modeling of individual catchments, which makes it difficult to generalize results to large domains and other hydrologic regimes. Instead, hydrologic synthesis across space and across many elements of hydrologic theory is needed, in order to improve the physical realism and general applicability of hydrologic models, i.e., to improve hydrologic process representations across a large range of catchments . To this end, some have argued (somewhat optimistically) that advances in modern hydrologic modeling efforts are possible through progress on the following fundamental research challenges: identifying consistently observed behaviors across research watersheds, formulating the laws that govern macroscale hydrologic behavior, and unifying process explanations across watersheds in order to develop theory of hydrology at the catchment scale [e.g., Dooge, 1986;Sivapalan, 2005;McDonnell et al., 2007].The needs of the hydrologic modeling community as articulated in this way are admittedly sizeable and potentially insurmountable. This has led others to adopt a rather pessimistic view, doubting if it is even possible to generalize hydrologic behaviors given the unique character of individual basins [Beven, 2000]. This raises the question, do we now, and/or will we always, lack the necessary information on climate, topography, vegetation, soils, and subsurface structure required to develop powerful and exceptionless explanations? Put differently, are the problems of underdetermination so pronounced that we cannot move Key Points: We seek to increase the physical realism of hydrologic models through better way existing theory We seek to improve the way models are used to integrate and eva...

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“…A listing of evolutionary organizing principles in plant sciences can be found in the review by Schymanski et al (2009a). The most well known example in hydrology is the use of ecological optimality principles by Eagleson (Eagleson, , 1978 Tellers, 1982) who focused on net primary production.…”

Section: Optimality Principlesmentioning

confidence: 99%

HESS Opinions: Hydrologic predictions in a changing environment: behavioral modeling

Schaefli

Harman

Sivapalan

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract. Most hydrological models are valid at most only in a few places and cannot be reasonably transferred to other places or to far distant time periods. Transfer in space is difficult because the models are conditioned on past observations at particular places to define parameter values and unobservable processes that are needed to fully characterize the structure and functioning of the landscape. Transfer in time has to deal with the likely temporal changes to both parameters and processes under future changed conditions. This remains an important obstacle to addressing some of the most urgent prediction questions in hydrology, such as prediction in ungauged basins and prediction under global change. In this paper, we propose a new approach to catchment hydrological modeling, based on universal principles that do not change in time and that remain valid across many places. The key to this framework, which we call behavioral modeling, is to assume that there are universal and time-invariant organizing principles that can be used to identify the most appropriate model structure (including parameter values) and responses for a given ecosystem at a given moment in time. These organizing principles may be derived from fundamental physical or biological laws, or from empirical laws that have been demonstrated to be time-invariant and to hold at many places and scales. Much fundamental research remains to be undertaken to help discover these organizing principles on the basis of exploration of observed patterns of landscape structure and hydrological behavior and their interpretation as legacy effects of past co-evolution of climate, soils, topography, vegetation and humans. Our hope is that the new behavioral modeling framework will be a step forward toCorrespondence to: B. Schaefli (bettina.schaefli@epfl.ch) wards a new vision for hydrology where models are capable of more confidently predicting the behavior of catchments beyond what has been observed or experienced before.

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Ecohydrological Optimality

Cited by 11 publications

References 63 publications

Using an optimality model to understand medium and long-term responses of vegetation water use to elevated atmospheric CO₂concentrations

Using an optimality model to understand medium and long-term responses of vegetation water use to elevated atmospheric CO₂concentrations

Improving the theoretical underpinnings of process‐based hydrologic models

HESS Opinions: Hydrologic predictions in a changing environment: behavioral modeling

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Ecohydrological Optimality

Cited by 11 publications

References 63 publications

Using an optimality model to understand medium and long-term responses of vegetation water use to elevated atmospheric CO2concentrations

Using an optimality model to understand medium and long-term responses of vegetation water use to elevated atmospheric CO2concentrations

Improving the theoretical underpinnings of process‐based hydrologic models

HESS Opinions: Hydrologic predictions in a changing environment: behavioral modeling

Contact Info

Product

Resources

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Using an optimality model to understand medium and long-term responses of vegetation water use to elevated atmospheric CO₂concentrations

Using an optimality model to understand medium and long-term responses of vegetation water use to elevated atmospheric CO₂concentrations