The Channel-Hillslope Integrated Landscape Development Model (CHILD)

Tucker, Gregory E.; Lancaster, S. T.; Gasparini, N. M.; Bras, Rafael L.

doi:10.1007/978-1-4615-0575-4_12

Cited by 246 publications

(276 citation statements)

References 51 publications

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“…As such, it requires both extension and synthesis of existing hydrologic modeling techniques. While existing mechanistic hydrologic models can be coupled, through the interdependence of their parameters, to models of other physical or biological systems (Flores et al, 2009;Qu and Duffy, 2007;Tucker et al, 2001;Beven, 2007), and while many existing land surface modeling schemes already link ecological, climatological and hydrological fluxes (Gerten, 2013), indiscriminately extending such complex models rapidly becomes intractable. Thus, other modeling approaches are essential.…”

Section: Co-evolutionary Modelingmentioning

confidence: 99%

“…Ultimately, we will wish to use detailed mechanistic (bottom up) models of coupled systems to generate quantitative predictive insights. Several such models already exist for different eco-hydrological, hydro-geomorphological and other applications (Flores et al, 2009;Qu and Duffy, 2007;Tucker et al, 2001). It is unclear, however, whether existing models are well prepared to cope with non-stationary systems (Wagener et al, 2010).…”

Section: Co-evolutionary Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Thompson

Sivapalan

Harman

et al. 2013

Hydrol. Earth Syst. Sci.

129

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Abstract.Globally, many different kinds of water resources management issues call for policy-and infrastructure-based responses. Yet responsible decision-making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadal-to 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.

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Section: Co-evolutionary Modelingmentioning

confidence: 99%

Section: Co-evolutionary Modelingmentioning

confidence: 99%

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Thompson

Sivapalan

Harman

et al. 2013

Hydrol. Earth Syst. Sci.

129

View full text Add to dashboard Cite

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“…Figure 1 shows a pair of numerical simulations that illustrate different versions of this scenario, using two different endmember hypotheses about how the landscape material erodes in response to runoff. Both were computed using the ChannelHillslope Integrated Landscape Development (CHILD) model (Tucker et al, 2001). In this model, the land surface is represented as a network of Voronoi polygons that are connected to form a Delaunay triangulation.…”

Section: Example: Two Models For the Evolution Of A Soil-mantled Draimentioning

confidence: 99%

Natural experiments in landscape evolution

Tucker

2009

Earth Surf Processes Landf

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A natural experiment in landscape evolution is a case study of landform development in which only one element varies significantly, and for which the driving forces, initial conditions, and/or boundary conditions are well constrained. Natural experiments provide a means of testing landscape evolution theory on the large space and time scales to which that theory applies. Natural experiments can involve either steady or transient conditions. Cases with steady conditions allow one to test predictions about the relationships among topography, erosion rates, and various attributes related to climate and material properties. Transient cases are valuable for distinguishing between models whose predictions might be similar, and therefore indistinguishable, under steady conditions. Essential ingredients of a natural experiment include minimal variation in all but one factor, good constraints on timing and/or rates, well-characterized processes, and high quality topographic data. Other useful ingredients include information about intermediate topographic states (such as a former valley profile revealed by strath terraces), and knowledge of the time history of erosion rates. In order to deepen our understanding of the physics and chemistry of longterm landscape evolution, there is a pressing need to identify natural experiments and develop the necessary databases to take advantage of them.

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“…Data Input: The availability of grid-based Digital Elevation Models (DEMs) has made regular two dimensional grids of cells the preferred landscape representation for LEMs. Alternative approaches discretize the landscape using a more irregular representation (CASCADE [7]), such as triangulated irregular networks (CHILD [2]). CybErosion adopts the more common format representing the landscape as a regular two dimensional grid of cells, each containing a single value representing the height of the land in that cell -we continue to use this approach.…”

Section: A Processing Pipeline For Cyberosionmentioning

confidence: 99%

“…Reduced complexity models, such as CHILD [2], CEASAR [3] or LAPSUS [4] all make significant, but different, model process or scale compromises, to achieve run times of a few days or less. Inevitably these compromises lead to unrealistic model behaviour, which often requires parametization of physically meaningless tuning variables in order to maintain numerical stability.…”

Section: Introductionmentioning

confidence: 99%

Massively parallel landscape-evolution modelling using general purpose graphical processing units

McGough

Liang

Rapoportas

et al. 2012

2012 19th International Conference on High Performance Computing

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Abstract-As our expectations of what computer systems can do and our ability to capture data improves, the desire to perform ever more computationally intensive tasks increases. Often these tasks, comprising vast numbers of repeated computations, are highly interdependent on each other -a closely coupled problem. The process of Landscape-Evolution Modelling is an example of such a problem. In order to produce realistic models it is necessary to process landscapes containing millions of data points over time periods extending up to millions of years. This leads to non-tractable execution times, often in the order of years. Researchers therefore seek multiple orders of magnitude reduction in the execution time of these models. The massively parallel programming environment offered through General Purpose Graphical Processing Units offers the potential for multiple orders of magnitude speedup in code execution times.In this paper we demonstrate how the time dominant parts of a Landscape-Evolution Model can be recoded for a massively parallel architecture providing two orders of magnitude reduction in execution time.

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The Channel-Hillslope Integrated Landscape Development Model (CHILD)

Cited by 246 publications

References 51 publications

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Natural experiments in landscape evolution

Massively parallel landscape-evolution modelling using general purpose graphical processing units

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