Evaluation of Conservation Practices through Simulation Modeling and Tool Development

For the first objective, predicted impacts of selected Mississippi River Basin Initiative (MRBI) conservation practices (CPs) on sediment and nutrient loss were assessed. The study area was L’Anguille River Watershed (LRW), a priority focus watershed of the MRBI program and the simulated CPs were filter strip, critical area planting, grade stabilization structure, irrigation land leveling, irrigation pipeline, irrigation water management, and nutrient management. The Soil and Water Assessment Tool (SWAT) model was calibrated (1998 – 2005) and validated (2006 – 2012) for flow, sediment, total phosphorus (TP), and nitrate-nitrogen (NO3-N) at the Colt site (503 sq. km. drainage area) and for total flow, surface flow, and base flow at the Palestine site (784 sq. km. drainage area). The statistical results for the calibration and validation were found to be satisfactory or better except a few root mean square error – standard deviations ratio (RSR) values for the calibration period. The SWAT model results were predicted from 2013 – 2017 for assessing the performance of CPs. Out of the CPs used in the LRW, critical area planting was the most effective in reducing the predicted nutrient (58% TP and 16% total nitrogen (TN)) and sediment (80%) loads, followed by filter strip, irrigation land leveling, grade stabilization structure, irrigation pipeline, irrigation water management, and nutrient management. Results such as these could help inform watershed planners and policy makers in selecting appropriate CPs that will most effectively bring about desired nutrient and sediment load reductions. For the second objective, a CP tool was developed with Python programming language for integrating a user-defined target area utilizing either a single or multiple selection criteria with the SWAT model. The tool uses open source packages such as Geospatial Data Abstraction Library (GDAL) and Matplotlib. The tool is standalone and was designed in such a way that it simulates CPs at the lowest simulation level (hydrological response unit) of the SWAT model by building a new targeting procedure for SWAT applications and decision-making. The tool automates the process for simulating CPs on a target area and analyzing differences between the baseline and CP scenario. The tool was evaluated for the Cache River Watershed (CRW). A target area was selected in the CRW and irrigation land leveling CP was simulated. A 22% decrease in sediment losses, 20% decrease in TP losses, and 12% decrease in TN losses were predicted. The tool provides a quick approach to address the water quality impacts on a specific target area. For the third objective, the Python-based CP tool developed for objective 2 was further updated to simulate CPs at user-defined locations using an interactive simulation approach. The tool allows the user to select the target area with mouse-clicks in a user-friendly and interactive environment. The LRW located in northeastern Arkansas was used as the test area. A target area was selected interactively in LRW and filter strip and irrigation land leveling CPs were simulated. A 70% decrease in sediment losses, 68% decrease in TP losses, and 47% decrease in TN losses were predicted

SWAT Model Simulation of Bioenergy Crop Impacts on Water Quality in Cache River Watershed

Energy security through increased biofuel production is one of the components of the Energy Independence and Security Act (EISA) 2007. As per EISA 2007 mandate, appropriate independent research institutes are required to assess concerns to natural biodiversity due to biofuel production and report it to the Congress through the Environment Protection Agency (EPA). Planners, researchers, and agencies concerned with environmental regulations, ideally, would like to have location-specific information about the impacts for developing appropriate management interventions. This study examines long-term impacts on water quality in response to targeted (i.e. marginal lands) production of biofuel crops by setting up two SWAT models. One of the SWAT model was set-up using typical modeling practice i.e. by using a single land use layer, whereas, the second SWAT model was set-up by incorporating dynamic land use change data. The Cache River Watershed in Arkansas, a watershed selected for Biomass Crop Assistance Program (BCAP) by the United States Department of Agriculture (USDA), was used for this case study. The crops of interest were Giant Miscanthus (Miscanthus x giganteus) and Switchgrass (Panicum virgatum L). Results indicated that sediment, total phosphorus and total nitrogen loadings decreased at the watershed outlet when these crops were cultivated on marginal crop lands thereby making them potentially useful for improving water quality in Cache River Watershed

Integrated Life Cycle Framework for Evaluating the Sustainability of Emerging Drop-In Replacement Biofuels

Mounting concerns over energy independence and security, oil supply volatility and price, and anthropogenic-derived climate destabilization are driving the strategic development of low-carbon biofuels. Recently, second generation biofuels—fuels derived from non-food biofeedstocks including: perennial grasses, short rotation woody crops (SRWCs), and microalgae have gained significant interest from scientific and political actors due to their potential for reduced life cycle greenhouse gas (GHG) emissions relative to baseline petroleum fuels, and fungibility with existing transportation infrastructure and vehicles fleets. However, the environmental sustainability of these second generation biofuels and their capacity to meet U.S. regulatory biofuel mandates remains uncertain, and a point of scientific inquiry. This work investigates the sustainability of emerging second-generation drop-in replacement hydrocarbon biofuels, utilizing sustainability metrics and methodologies derived from multiple disciplines including life cycle assessment, industrial ecology, statistics, thermodynamics, and process modeling. This novel interdisciplinary life cycle framework is applied to study the environmental sustainability of several distinct emerging drop-in replacement biofuel platforms including: (1) cultivation of microalgae in open raceways ponds and hydro-processing of algal-oil to renewable diesel, (2) fast pyrolysis of perennial grasses and hydro-upgrading of bio-oil to green gasoline, and (3) multistage torrefaction of SRWCs and catalytic upgrading to hydrocarbon biofuels. Traditional process-based Life Cycle Assessment (LCA) and hybrid Ecologically-based Life Cycle Assessment (EcoLCA) models are developed to assess the degradation of ecological good and services, environmental impacts, and resource intensity of producing drop-in replacement biofuels. Rigorous process modeling and statistical analysis is performed to quantify key sustainability metrics including energy return on investment and life cycle GHG emissions for producing hydrocarbon biofuels under different combinations of biofeedstocks, fuel upgrading pathways, and coproduct scenarios, and to determine if renewable fuel(s) meet compliance with life cycle GHG emissions reductions thresholds set by U.S. federal regulatory programs. This interdisciplinary approach captures broader environmental externalities and unintended consequences of biofuel production that are outside the purview of traditional process design, and allows for holistic understanding of the potential tradeoffs, challenges, and broad-based impacts of emerging biofuels prior to their widespread commercialization—information that is pivotal for guiding the sustainable development of the nascent biofuels industry

Estudio de aplicabilidad del modelo SWAT para la gestión hidrológica de cuencas de montaña

The mountain basins of Catalonia have seen their forest area progressively increased in recent decades. Having tools that allow us to assess the impact of changes in land use on water resources is essential for proper management and planning. In this sense, this work has configured, calibrated and validated the ‘Soil and Water Assessment Tool’ (SWAT) hydrological model in the tributary sub-basin of La Baells Reservoir, a mountain basin located halfway between the Pyrenees and the Catalan Pre-Pyrenees belonging to the Llobregat River basin. A "Split-sample test" type Calibration-Validation has been followed with flow data restored to the natural regime at the outlet of the reservoir, with a calibration period from 01/01/1988 to 12/31/1999 (12 years) and 12 years of warming up, and with a validation period from 01/01/1978 to 12/31/1987 (10 years) and 2 years of warming up. After identifying the parameters that provided the greatest improvement in behavior, combinations of these were tested, increasing the number 1 by 1 in the order of individual improvement. The best ones from these combinations of parameters in terms of statistics about the behavior of the model in the calibration period were selected, and then the calibrated model was run according to these configurations in the validation period, choosing the configuration with the best behavior both in the validation and calibration period. The chosen configuration has consisted in the relative change of the values of the parameters SOL_AWC (.sol), SOL_Z (.sol), SOL_CBN (.sol) of 0.444773, 0.7875 and 4.41853 respectively, and in the replacement of the values of CO2 (.sub) and LAI_INIT (.mgt, {[],1} (Planting)) by 316 and 52.5 respectively. The study also evaluated the effects of: a) introducing only meteorological data for temperature and precipitation (Spain02_v5 grid of Universidad de Cantabria (UC) and Agencia Estatal de Meteorología (AEMET)), b) introducing this meteorological data by creating virtual stations at the centroids of the sub-basins based on the proportion of the area of influence of the grid points with data according to the accumulated cost taking into account the orography, or c) the use of more complete data interpolated for the centroids of the sub-basins using the Meteoland App tool of the Laboratori Forestal Català (Centre de Recerca Ecològica i Aplicacions Forestals ‘ Centre de Ciència i Tecnologia Forestal de Catalunya, CREAF-CTFC). In this sense, the third option yielded the best results, with average improvements in the statistics of 14.11% and 23.86%, respectively, compared to the first option during the calibration period for the uncalibrated model. As a result of this work, a very good evaluation of the behavior of the model has been obtained for the calibration period according to Coefficient of Determination (R2) (0.86), ‘Nash-Sutcliffe Efficiency’ coefficient (NSE) (0.81), ‘Ratio of Standard Deviation of Observation to Root Mean Square Error’ (RSR) (0.44) and ‘Index of Agreement’ (d) (0.96), and good according to ‘Percent Bias’ (PBIAS) (6.7%), with average improvements in the statistics of 32.48% compared to the uncalibrated model results, while very good according to PBIAS (0.1%) and d (0.92), good according to R2 (0.81) and satisfactory according to NSE (0.53) and RSR (0.68) for the validation period, with average improvements in the statistics of 28.05% compared to the uncalibrated model results. In this way, we can affirm that the SWAT hydrological model can be considered a useful and robust tool for estimating flows in the study basin, and thereby, for quantifying the effects that changes in climate and/or land use could have in them.Les conques de muntanya de Catalunya estan veient incrementada la seva superfície forestal de manera progressiva a les darreres dècades. Comptar amb eines que ens permetin avaluar l'impacte dels canvis d'usos de sòl als recursos hídrics és essencial per a una ordenació i planificació correctes. En aquest sentit, aquest treball ha configurat, calibrat i validat el model hidrològic ‘Soil and Water Assessment Tool’ (SWAT) a la subconca tributària de l'Embassament de la Baells, una conca de muntanya ubicada a cavall entre el Pirineu i el Prepirineu català pertanyent a la conca del Riu Llobregat. S'ha seguit un Calibratge-Validació del tipus ‘Split-sample test’ amb dades de cabal restituïdes a règim natural a la sortida de l'embassament, amb un període de calibratge del 01/01/1988 al 31/12/1999 (12 anys) i 12 anys d'escalfament, i amb un període de validació del 01/01/1978 al 31/12/1987 (10 anys) i 2 anys d'escalfament. Després d'identificar els paràmetres que més millora del comportament proporcionaven, se'n van provar combinacions, incrementant el nombre d'1 en 1 en l'ordre de millora individual. D'aquestes combinacions de paràmetres, es van seleccionar les millors en quant a estadístics sobre el comportament del model en el període de calibratge, i a continuació, es va córrer el model calibrat segons aquestes configuracions en el període de validació, escollint la configuració amb millor comportament tant pel període de validació com de calibratge. La configuració escollida ha consistit en el canvi relatiu dels valors dels paràmetres SOL_AWC (.sol), SOL_Z (.sol), SOL_CBN (.sol) de 0.444773, 0.7875 i 4,41853 respectivament, i en el reemplaçament dels valors de CO2 (.sub) i LAI_INIT (.mgt, {[],1} (Planting)) per 316 i 52.5 respectivament. També es van avaluar els efectes de: a) introduir dades meteorològiques només de temperatura i precipitació (quadrícula Spain02_v5 de la Universidad de Cantabria (UC) i l’Agencia Estatal de Meteorología (AEMET)), b) fer-ho creant estacions virtuals als centroides de les subconques en funció de la proporció de l'àrea d'influència dels punts de la quadrícula amb dades en relació al cost acumulat en tenir en compte l'orografia, o c) utilitzar dades més completes interpolades per als centroides de les subconques mitjançant l'eina Meteoland App del Laboratori Forestal Català (Centre de Recerca Ecològica i Aplicacions Forestals ‘ Centre de Ciència i Tecnologia Forestal de Catalunya, CREAF-CTFC). En aquest sentit, la tercera opció va donar els millors resultats, amb millores mitjanes en els estadístics del 14,11% i del 23,86% respectivament respecte a la primera opció durant el període de calibratge per al model sense calibrar. Com a resultat d'aquest treball, s'ha obtingut una valoració del comportament del model per al període de calibratge molt bona segons el Coeficient de Determinació (R2) (0.86), el coeficient ‘Nash-Sutcliffe Efficiency’ (NSE) (0.81), el ‘Ratio of Standard Deviation of Observation to Root Mean Square Error’ (RSR) (0.44) i el ‘Index of Agreement’ (d) (0.96), i bona segons el ‘Percent Bias’ (PBIAS) (6.7%), amb millores mitjanes en els estadístics del 32.48% respecte al model sense calibrar, mentre que molt bona segons PBIAS (0.1%) i d (0.92), bona segons R2 (0.81) i satisfactòria segons NSE (0.53) i RSR (0.68) per al període de validació, amb millores mitjanes en els estadístics del 28.05% respecte al model sense calibrar. D'aquesta manera, podem afirmar que el model hidrològic SWAT es pot considerar una eina útil i robusta per a l'estimació dels cabals a la conca d'estudi, i així, quantificar els efectes que canvis en el clima i/o usos del sòl poguessin tenir en aquests.Las cuencas de montaña de Cataluña están viendo incrementada su superficie forestal de manera progresiva en las últimas décadas. Contar con herramientas que nos permitan evaluar el impacto de los cambios de usos de suelo en los recursos hídricos es esencial para una correcta ordenación y planificación. En este sentido, este trabajo ha configurado, calibrado y validado el modelo hidrológico ‘Soil and Water Assessment Tool’ (SWAT) en la subcuenca tributaria del Embalse de La Baells, una cuenca de montaña ubicada a caballo entre el Pirineo y el Prepirineo catalán perteneciente a la cuenca del Río Llobregat. Se ha seguido una Calibración-Validación del tipo ‘Split-sample test’ con datos de caudal restituidos a régimen natural a la salida del embalse, con un periodo de calibración del 01/01/1988 al 31/12/1999 (12 años) y 12 años de calentamiento, y con un periodo de validación del 01/01/1978 al 31/12/1987 (10 años) y 2 años de calentamiento. Tras identificar los parámetros que mayor mejora del comportamiento proporcionaban, se probaron combinaciones de éstos, incrementando el número de 1 en 1 en el orden de mejora individual. De éstas combinaciones de parámetros, se seleccionaron las mejores en cuanto a estadísticos sobre el comportamiento del modelo en el periodo de calibración, y a continuación, se corrió el modelo calibrado según estas configuraciones en el periodo de validación, escogiéndose la configuración con mejor comportamiento tanto en el periodo de validación como de calibración. La configuración escogida ha consistido en el cambio relativo de los valores de los parámetros SOL_AWC (.sol), SOL_Z (.sol), SOL_CBN (.sol) de 0.444773, 0.7875 y 4,41853 respectivamente, y en el reemplazo de los valores de CO2 (.sub) y LAI_INIT (.mgt, {[],1} (Planting)) por 316 y 52.5 respectivamente. También se evaluaron los efectos de: a) introducir datos meteorológicos solo de temperatura y precipitación (rejilla Spain02_v5 de la Universidad de Cantabria (UC) y la Agencia Estatal de Meteorología (AEMET)), b) hacerlo creando estaciones virtuales en los centroides de las subcuencas en función de la proporción del área de influencia de los puntos de la rejilla con datos en relación al coste acumulado al tener en cuenta la orografía, o c) utilizar datos más completos interpolados para los centroides de las subcuencas mediante la herramienta Meteoland App del Laboratori Forestal Català (Centre de Recerca Ecològica i Aplicacions Forestals ‘ Centre de Ciència i Tecnologia Forestal de Catalunya, CREAF-CTFC). En este sentido, la tercera opción arrojó los mejores resultados, con mejoras medias en los estadísticos del 14,11% y del 23,86% respectivamente respecto la primera opción durante el periodo de calibración para el modelo sin calibrar. Como resultado de este trabajo, se ha obtenido una valoración del comportamiento del modelo para el periodo de calibración muy buena según el Coeficiente de Determinación (R2) (0.86), el coeficiente ‘Nash-Sutcliffe Efficiency’ (NSE) (0.81), el ‘Ratio of Standard Deviation of Observation to Root Mean Square Error’ (RSR) (0.44) y el ‘Index of Agreement’ (d) (0.96), y buena según el ‘Percent Bias’ (PBIAS) (6.7%), con mejoras medias de los estadísticos del 32.48% respecto al modelo sin calibrar, mientras que muy buena según PBIAS (0.1%) y d (0.92), buena según R2 (0.81) y satisfactoria según NSE (0.53) y RSR (0.68) para el periodo de validación, con mejoras medias de los estadísticos del 28.05% respecto al modelo sin calibrar. De esta manera, podemos afirmar que el modelo hidrológico SWAT puede considerarse una herramienta útil y robusta para la estimación de los caudales en la cuenca de estudio, y con ello, cuantificar los efectos que cambios en el clima y/o usos del suelo pudieran tener en éstos.Objectius de Desenvolupament Sostenible::13 - Acció per al ClimaObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenible

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)