Person:
Muñoz Carpena, Rafael

person.page.identifierURI

https://academica-e.unavarra.es/handle/2454/49632

Last Name

Muñoz Carpena

First Name

Rafael

person.page.departamento

Ingeniería

ORCID

0000-0003-2838-1514

person.page.upna

811780

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Now showing 1 - 10 of 10

Open Access
Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter strips
(Elsevier, 2023) Reichenberger, Stefan; Sur, Robin; Sittig, Stephan; Multsch, Sebastián; Carmona Cabrero, Álvaro; López Rodríguez, José Javier; Muñoz Carpena, Rafael; Ingeniería; Ingeniaritza; Universidad Pública de Navarra / Nafarroako Unibertstitate Publikoa
The most widely implemented mitigation measure to reduce transfer of surface runoff pesticides and other pollutants to surface water bodies are vegetative filter strips (VFS). The most commonly used dynamic model for quantifying the reduction by VFS of surface runoff, eroded sediment, pesticides and other pollutants is VFSMOD, which simulates reduction of total inflow (ΔQ) and of incoming eroded sediment load (ΔE) mechanistically during the rainfall-runoff event. These variables are subsequently used to calculate the reduction of pesticide load by the VFS (ΔP). Since errors in ΔQ and ΔE propagate into ΔP, for strongly-sorbing compounds an accurate prediction of ΔE is crucial for a reliable prediction of ΔP. The most important incoming sediment characteristic for ΔE is the median particle diameter (d50). Current d50 estimation methods are simplistic, yielding fixed d50 based on soil properties and ignoring specific event characteristics and dynamics. We derive an improved dynamic d50 parameterization equation for use in regulatory VFS scenarios based on an extensive dataset of 93 d50 values and 17 candidate explanatory variables compiled from heterogeneous data sources and methods. The dataset was analysed first using machine learning techniques (Random Forest, Gradient Boosting) and Global Sensitivity Analysis (GSA) as a dimension reduction technique and to identify potential interactions between explanatory variables. Using the knowledge gained, a parsimonious multiple regression equation with 6 predictors was developed and thoroughly tested. Since three of the predictors are eventspecific (eroded sediment yield, rainfall intensity and peak runoff rate), predicted d50 vary dynamically across event magnitudes and intensities. Incorporation of the improved d50 parameterization equation in higher-tier pesticide assessment tools with VFSMOD provides more realistic quantitative mitigation in regulatory US-EPA and EU FOCUS pesticide risk assessment frameworks. The equation is also readily applicable to other erosion management problems.
Open Access
Combined spatial and temporal effects of environmental controls on long-term monthly NDVI in the Southern Africa savanna
(MDPI, 2013) Campo-Bescós, Miguel; Muñoz Carpena, Rafael; Southworth, Jane; Zhu, Likai; Waylen, Peter; Bunting, Erin; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Deconstructing the drivers of large-scale vegetation change is critical to predicting and managing projected climate and land use changes that will affect regional vegetation cover in degraded or threated ecosystems. We investigate the shared dynamics of spatially variable vegetation across three large watersheds in the southern Africa savanna. Dynamic Factor Analysis (DFA), a multivariate time-series dimension reduction technique, was used to identify the most important physical drivers of regional vegetation change. We first evaluated the Advanced Very High Resolution Radiometer (AVHRR)- vs. the Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) datasets across their overlapping period (2001–2010). NDVI follows a general pattern of cyclic seasonal variation, with distinct spatio-temporal patterns across physio-geographic regions. Both NDVI products produced similar DFA models, although MODIS was simulated better. Soil moisture and precipitation controlled NDVI for mean annual precipitation (MAP) < 750 mm, and above this, evaporation and mean temperature dominated. A second DFA with the full AVHRR (1982–2010) data found that for MAP < 750 mm, soil moisture and actual evapotranspiration control NDVI dynamics, followed by mean and maximum temperatures. Above 950 mm, actual evapotranspiration and precipitation dominate. The quantification of the combined spatio-temporal environmental drivers of NDVI expands our ability to understand landscape level changes in vegetation evaluated through remote sensing and improves the basis for the management of vulnerable regions, like the southern Africa savannas.
Open Access
Using a coupled dynamic factor-random forest analysis (DFRFA) to reveal drivers of spatiotemporal heterogeneity in the semi-arid regions of southern Africa
(Public Library of Science, 2018) Southworth, Jane; Bunting, Erin; Zhu, Likai; Ryan, Sadie J.; Herrero, Hannah V.; Waylen, Peter; Muñoz Carpena, Rafael; Campo-Bescós, Miguel; Kaplan, David A.; Ingeniería; Ingeniaritza
Understanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude, and spatial distribution of the key environmental and socioeconomic factors driving vegetation change in a southern African savanna. This research was conducted across the Kwando, Okavango and Zambezi catchments of southern Africa (Angola, Namibia, Botswana and Zambia) and explored vegetation cover change across the region from 2001–2010. A novel coupled analysis was applied to model the dynamic biophysical factors then to determine the discrete / social drivers of spatial heterogeneity on vegetation. Previous research applied Dynamic Factor Analysis (DFA), a multivariate times series dimension reduction technique, to ten years of monthly remotely sensed vegetation data (MODIS-derived normalized difference vegetation index, NDVI), and a suite of time-series (monthly) environmental covariates: precipitation, mean, minimum and maximum air temperature, soil moisture, relative humidity, fire and potential evapotranspiration. This initial research was performed at a regional scale to develop meso-scale models explaining mean regional NDVI patterns. The regional DFA predictions were compared to the fine-scale MODIS time series using Kendall’s Tau and Sen’s Slope to identify pixels where the DFA model we had developed, under or over predicted NDVI. Once identified, a Random Forest (RF) analysis using a series of static social and physical variables was applied to explain these remaining areas of under- and over- prediction to fully explore the drivers of heterogeneity in this savanna system. The RF analysis revealed the importance of protected areas, elevation, soil type, locations of higher population, roads, and settlements, in explaining fine scale differences in vegetation biomass. While the previously applied DFA generated a model of environmental variables driving NDVI, the RF work developed here highlighted human influences dominating that signal. The combined DFRFA model approach explains almost 90% of the variance in NDVI across this landscape from 2001–2010. Our methodology presents a unique coupling of dynamic and static factor analyses, yielding novel insights into savanna heterogeneity, and providing a tool of great potential for researchers and managers alike.
Open Access
Model prediction capacity of ephemeral gully evolution in conservation tillage systems
(Wiley, 2021) Luquin Oroz, Eduardo Adrián; Campo-Bescós, Miguel; Muñoz Carpena, Rafael; Bingner, R.L.; Cruse, Richard M.; Momm, Henrique G.; Wells, R.; Casalí Sarasíbar, Javier; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ingeniería; Gobierno de Navarra / Nafarroako Gobernua; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Ephemeral gully (EG) erosion has an important impact on agricultural soil losses and increases field surface hydrology connectivity and transport of pollutants to nearby water bodies. Watershed models including an EG component are scarce and not yet properly evaluated. The objective of this study is to evaluate the capacity of one such tool, AnnAGNPS, to simulate the evolution of two EG formed in a conservation tillage system. The dataset for model testing included runoff measurements and EG morphological characteristics during 3 years. Model evaluation focused on EG evolution of volume, width, and length model outputs, and included calibration and testing phases and a global sensitivity analysis (GSA). While the model did not fully reproduce width and length, the model efficiency to simulate EG volume was satisfactory for both calibration and testing phases, supporting the watershed management objectives of the model. GSA revealed that the most sensitive factors were EG depth, critical shear stress, headcut detachment exponent coefficient b, and headcut detachment leading coefficient a. For EG outputs the model was additive, showing low sensitivity to interactions between the inputs. Prediction of EG spatial evolution on conservation tillage systems requires improved development of gullyerosion components, since many of the processes were developed originally for traditional tillage practices or larger channel systems. Our results identify the need for future research when EG form within conservation tillage systems, in particular to study gully headcut, soil erodibility, and width functions specific to these practices.
Open 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.
Open 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.
Open Access
Highway paving in the southwestern Amazon alters long-term trends and drivers of regional vegetation dynamics
(Elsevier, 2018) Klarenberg, Geraldine; Muñoz Carpena, Rafael; Campo-Bescós, Miguel; Perz, Steve G.; Ingeniería; Ingeniaritza
Infrastructure development, specifically road paving, contributes socio-economic benefits to society worldwide. However, detrimental environmental effects of road paving have been documented, most notably increased deforestation. Beyond deforestation, we hypothesize that road paving introduces “unseen” regional scale effects on forests, due to changes to vegetation dynamics. To test this hypothesis, we focus on the tri-national frontier in the southwestern Amazon that has been subject to construction of the Inter-Oceanic Highway (IOH) between 1987 and 2010. We use a long-term remotely sensed vegetation index as a proxy for vegetation dynamics and combine these with field-based socio-ecological data and biophysical data from global datasets. We find 4 areas of shared vegetation dynamics associated with increasing extent of road paving. Applying Dynamic Factor Analysis, an exploratory dimension-reduction time series analysis technique, we identify common trends and covariates in each area. Common trends, indicating underlying unexplained effects, become relatively less important as paving increases, and covariates increase in importance. The common trends are dominated by lower frequency signals possibly embodying long-term climate variability. Human-related covariates become more important in explaining vegetation dynamics as road paving extent increases, particularly family density and travel time to market. Natural covariates such as minimum temperature and soil moisture become less important. The change in vegetation dynamics identified in this study indicates a possible change in ecosystem services along the disturbance gradient. While this study does not include all potential factors controlling dynamics and disturbance of vegetation in the region, it offers important insights for management and mitigation of effects of road paving projects. Infrastructure planning initiatives should make provisions for more detailed vegetation monitoring after road completion, with a broader focus than just deforestation. The study highlights the need to mitigate population-driven pressures on vegetation like family density and access to new markets.
Open Access
Watering or buffering? Runoff and sediment pollution control from furrow irrigated fields in arid environments
(Elsevier, 2015) Campo-Bescós, Miguel; Muñoz Carpena, Rafael; Kiker, Gregory A.; Bodah, Brian W.; Ullman, Jeffrey L.; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Surface irrigated agriculture in arid and semi-arid regions contributes to downstream environmental degradation. Changes in irrigation system operational scenarios (ISOS) can represent an economic alternative to reduce surface runoff impacts. At the same time the use of vegetative filter strips (VFS) can have a positive impact on the ecological health of rural landscapes by reducing erosion, improving water quality, increasing biodiversity, and expanding wildlife habitat. The goal of this paper is, using a combination of field data and mechanistic modeling results, to evaluate and compare the spatial effectiveness of improvements in ISOS and introduction of VFS to reduce surface runoff pollution in the semi-arid/arid furrow irrigation agroecosystem that exceeds current regulatory turbidity limits (25 NTU). Five main factor interactions were studied: four soil textures, two field slopes, three ISOS, six filter vegetation types, and ten filter lengths. Slope and runoff volume were identified as the two main drivers of sediment export from furrows. Shifting from current ISOS to less water consumptive irrigation practices reduce runoff in addition to sediment delivery to comply with environmental regulations. The implementation of 3 to 9 m vegetative buffers on experimental parcels were found to mitigate sediment delivery (greater than 90% sediment reduction) on tail drainage ditches but had limited effect in the reduction of runoff flow that can transport other dissolved pollutants. These findings were insensitive to filter vegetation type. Thus, introduction of improved ISOS is desirable while VFS may be targeted to specific hot spots within the irrigation district. This study shows that the adoption of dense vegetation buffers in vulnerable semi-arid irrigated regions can be effective to mitigate agricultural impacts and provide environmental protection. However, it should not be adopted as an alternative to proper on-site irrigation practices, rather as a complementary off-site pollution control practice.
Open Access
Beyond precipitation: physiographic gradients dictate the relative importance of environmental drivers on savanna vegetation
(Public Library of Science, 2013) Campo-Bescós, Miguel; Muñoz Carpena, Rafael; Kaplan, David A.; Southworth, Jane; Zhu, Likai; Waylen, Peter; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Background: Understanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude and spatial distribution of the key environmental factors driving vegetation change in southern African savanna, and how they vary across physiographic gradients. Methodology/Principal Findings: We applied Dynamic Factor Analysis (DFA), a multivariate times series dimension reduction technique to ten years of monthly remote sensing data (MODIS-derived normalized difference vegetation index, NDVI) and a suite of environmental covariates: precipitation, mean and maximum temperature, soil moisture, relative humidity, fire and potential evapotranspiration. Monthly NDVI was described by cyclic seasonal variation with distinct spatiotemporal patterns in different physiographic regions. Results support existing work emphasizing the importance of precipitation, soil moisture and fire on NDVI, but also reveal overlooked effects of temperature and evapotranspiration, particularly in regions with higher mean annual precipitation. Critically, spatial distributions of the weights of environmental covariates point to a transition in the importance of precipitation and soil moisture (strongest in grass-dominated regions with precipitation,750 mm) to fire, potential evapotranspiration, and temperature (strongest in tree-dominated regions with precipitation.950 mm). Conclusions/Significance: We quantified the combined spatiotemporal effects of an available suite of environmental drivers on NDVI across a large and diverse savanna region. The analysis supports known drivers of savanna vegetation but also uncovers important roles of temperature and evapotranspiration. Results highlight the utility of applying the DFA approach to remote sensing products for regional analyses of landscape change in the context of global environmental change. With the dramatic increase in global change research, this methodology augurs well for further development and application of spatially explicit time series modeling to studies at the intersection of ecology and remote sensing.
Open 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.