Groundwater–soil water–vegetation dynamics in a temperate forest ecosystem along a slope

Brolsma, Reinder; Bierkens, Marc F. P.

doi:10.1029/2005wr004696

Cited by 46 publications

(43 citation statements)

References 29 publications

(31 reference statements)

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“…Levin, 1992;RodriguezIturbe et al, 1992a;Thomas, 2001;Gisiger, 2001;Sivapalan, 2005), and a range of models that reproduce observed patterns and feedbacks are available (see e.g. Borgogno et al, 2009;Rodriguez-Iturbe et al, 2007). Saco et al (2006), for example, present a model that, in water-limited ecosystems, reproduces observed patterns of vegetation, runoff, erosion and their redistribution, and the evolution of microtopography.…”

Section: Structure and Organization In Catchmentsmentioning

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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Section: Structure and Organization In Catchmentsmentioning

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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“…The growth of vascular plants may promote an increase in transpiration rates (Frankl and Schmeidl 2000;Andersen et al 2005). We assumed that transpiration could be reduced due to water stress (Rietkerk et al 2004a;Ridolfi et al 2006;Brolsma and Bierkens 2007). Therefore, we applied the same water stress function for transpiration as used for plant growth (table 2).…”

Section: Mechanism 3: Nutrient Accumulationmentioning

confidence: 99%

“…A in the online edition of the American Naturalist, which also includes apps. B-G, for analytical details; Brolsma and Bierkens 2007).…”

Section: Model System and Study Approachmentioning

confidence: 99%

Nutrients and Hydrology Indicate the Driving Mechanisms of Peatland Surface Patterning

Eppinga¹,

Ruiter²,

Wassen³

et al. 2009

The American Naturalist

132

183

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Peatland surface patterning motivates studies that identify underlying structuring mechanisms. Theoretical studies so far suggest that different mechanisms may drive similar types of patterning. The long time span associated with peatland surface pattern formation, however, limits possibilities for empirically testing model predictions by field manipulations. Here, we present a model that describes spatial interactions between vegetation, nutrients, hydrology, and peat. We used this model to study pattern formation as driven by three different mechanisms: peat accumulation, water ponding, and nutrient accumulation. By on-and-off switching of each mechanism, we created a full-factorial design to see how these mechanisms affected surface patterning (pattern of vegetation and peat height) and underlying patterns in nutrients and hydrology. Results revealed that different combinations of structuring mechanisms lead to similar types of peatland surface patterning but contrasting underlying patterns in nutrients and hydrology. These contrasting underlying patterns suggest that the presence or absence of the structuring mechanisms can be identified by relatively simple short-term field measurements of nutrients and hydrology, meaning that longerterm field manipulations can be circumvented. Therefore, this study provides promising avenues for future empirical studies on peatland patterning.

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“…For example, Rodriguez-Iturbe et al (1999), Porporato et al (2001) and Caylor et al (2009) minimized water stress as the objective function for vegetation in (semi-)arid areas. Maximum transpiration and minimal water and oxygen stress have been used by Brolsma and Bierkens (2007), who simulated the competition between two vegetation species, while…”

Section: Introductionmentioning

confidence: 99%

Maximum entropy production: can it be used to constrain conceptual hydrological models?

Westhoff

Zehe

2013

Hydrol. Earth Syst. Sci.

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Abstract. In recent years, optimality principles have been proposed to constrain hydrological models. The principle of maximum entropy production (MEP) is one of the proposed principles and is subject of this study. It states that a steady state system is organized in such a way that entropy production is maximized. Although successful applications have been reported in literature, generally little guidance has been given on how to apply the principle. The aim of this paper is to use the maximum power principle -which is closely related to MEP -to constrain parameters of a simple conceptual (bucket) model. Although, we had to conclude that conceptual bucket models could not be constrained with respect to maximum power, this study sheds more light on how to use and how not to use the principle. Several of these issues have been correctly applied in other studies, but have not been explained or discussed as such.While other studies were based on resistance formulations, where the quantity to be optimized is a linear function of the resistance to be identified, our study shows that the approach also works for formulations that are only linear in the logtransformed space. Moreover, we showed that parameters describing process thresholds or influencing boundary conditions cannot be constrained. We furthermore conclude that, in order to apply the principle correctly, the model should be (1) physically based; i.e. fluxes should be defined as a gradient divided by a resistance, (2) the optimized flux should have a feedback on the gradient; i.e. the influence of boundary conditions on gradients should be minimal, (3) the temporal scale of the model should be chosen in such a way that the parameter that is optimized is constant over the modelling period, (4) only when the correct feedbacks are implemented the fluxes can be correctly optimized and (5) there should be a trade-off between two or more fluxes. Although our application of the maximum power principle did not work, and although the principle is a hypothesis that should still be thoroughly tested, we believe that the principle still has potential in advancing hydrological science.

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Groundwater–soil water–vegetation dynamics in a temperate forest ecosystem along a slope

Cited by 46 publications

References 29 publications

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

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

Nutrients and Hydrology Indicate the Driving Mechanisms of Peatland Surface Patterning

Maximum entropy production: can it be used to constrain conceptual hydrological models?

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