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Muñoz Carpena, Rafael

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Muñoz Carpena

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Rafael

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Ingeniería

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0000-0003-2838-1514

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811780

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2016 - 201912020 - 20242
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Author: Huffaker, Ray ×

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Now showing 1 - 3 of 3
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    PublicationOpen Access
    Demonstrating correspondence between decision-support models and dynamics of real-world environmental systems
    (Elsevier, 2016) Huffaker, Ray; Muñoz Carpena, Rafael; Campo-Bescós, Miguel; Southworth, Jane; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
    There are increasing calls to audit decision-support models used for environmental policy to ensure that they correspond with the reality facing policy makers. Modelers can establish correspondence by providing empirical evidence of real-world behavior that their models skillfully simulate. Since real-world behavior—especially in environmental systems—is often complex, credibly modeling underlying dynamics is essential. We present a pre-modeling diagnostic framework based on Nonlinear Time Series (NLTS) methods for reconstructing real-world environmental dynamics from observed data. The framework is illustrated with a case study of saltwater intrusion into coastal wetlands in Everglades National Park, Florida, USA. We propose that environmental modelers test for systematic dynamic behavior in observed data before resorting to conventional stochastic exploratory approaches unable to detect this valuable information. Reconstructed data dynamics can be used, along with other expert information, as a rigorous benchmark to guide specification and testing of environmental decision-support models corresponding with real-world behavior.
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    PublicationOpen Access
    Hydrological records can be used to reconstruct the resilience of watersheds to climatic extremes
    (Nature Research, 2024) Huffaker, Ray; Campo-Bescós, Miguel; Luquin Oroz, Eduardo Adrián; Casalí Sarasíbar, Javier; Muñoz Carpena, Rafael; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
    Hydrologic resilience modeling is used in public watershed management to assess watershed ability to supply life-supporting ecoservices under extreme climatic and environmental conditions. Literature surveys criticize resilience models for failing to capture watershed dynamics and undergo adequate testing. Both shortcomings compromise their ability to provide management options reliably protecting water security under real-world conditions. We formulate an empirical protocol to establish real-world correspondence. The protocol applies empirical nonlinear dynamics to reconstruct hydrologic dynamics from watershed records, and analyze the response of reconstructed dynamics to extreme regional climatic conditions. We devise an AI-based early-warning system to forecast (out-of-sample) reconstructed hydrologic resilience dynamics. Application to the La Tejería (Spain) experimental watershed finds it to be a low dimensional nonlinear deterministic dynamic system responding to internal stressors by irregularly oscillating along a watershed attractor. Reconstructed and forecasted hydrologic resilience behavior faithfully captures monthly wet-cold/dry-warm weather patterns characterizing the Mediterranean region.
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    PublicationOpen Access
    Experimental evidence that rill-bed morphology is governed by emergent nonlinear spatial dynamics
    (Springer Nature, 2022) Morgan, Savannah; Huffaker, Ray; Giménez Díaz, Rafael; Campo-Bescós, Miguel; Muñoz Carpena, Rafael; Govers, G.; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
    Past experimental work found that rill erosion occurs mainly during rill formation in response to feedback between rill-flow hydraulics and rill-bed roughness, and that this feedback mechanism shapes rill beds into a succession of step-pool units that self-regulates sediment transport capacity of established rills. The search for clear regularities in the spatial distribution of step-pool units has been stymied by experimental rill-bed profiles exhibiting irregular fluctuating patterns of qualitative behavior. We hypothesized that the succession of step-pool units is governed by nonlinear-deterministic dynamics, which would explain observed irregular fluctuations. We tested this hypothesis with nonlinear time series analysis to reverse-engineer (reconstruct) state-space dynamics from fifteen experimental rill-bed profiles analyzed in previous work. Our results support this hypothesis for rill-bed profiles generated both in a controlled lab (flume) setting and in an in-situ hillside setting. The results provide experimental evidence that rill morphology is shaped endogenously by internal nonlinear hydrologic and soil processes rather than stochastically forced; and set a benchmark guiding specification and testing of new theoretical framings of rill-bed roughness in soil-erosion modeling. Finally, we applied echo state neural network machine learning to simulate reconstructed rill-bed dynamics so that morphological development could be forecasted out-of-sample.