Intercomparison and Uncertainty Assessment of Nine Evapotranspiration Estimates Over South America

This study examines the uncertainties and the representations of anomalies of a set of evapotranspiration products over climatologically distinct regions of South America. The products, coming from land surface models, reanalysis, and remote sensing, are chosen from sources that are readily available to the community of users. The results show that the spatial patterns of maximum uncertainty differ among metrics, with dry regions showing maximum relative uncertainties of annual mean evapotranspiration, while energy-limited regions present maximum uncertainties in the representation of the annual cycle and monsoon regions in the representation of anomalous conditions. Furthermore, it is found that land surface models driven by observed atmospheric fields detect meteorological and agricultural droughts in dry regions unequivocally. The remote sensing products employed do not distinguish all agricultural droughts and this could be attributed to the forcing net radiation. The study also highlights important characteristics of individual data sets and recommends users to include assessments of sensitivity to evapotranspiration data sets in their studies, depending on region and nature of study to be conducted.Fil: Sörensson, Anna. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Ruscica, Romina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentin

Summer soil-precipitation coupling in South America

The soil moisture memory contributes to atmospheric variability and seasonal predictability and could potentially affect the development of the South American Monsoon System. The relative importance of the local land surface feedbacks and the large-scale dynamical processes during the different phases of the monsoon are still largely unknown. We examine the impacts of land surface conditions during the mature monsoon phase with the Rossby Centre Atmospheric regional model through calculating the coupling strength between soil moisture, evapotranspiration and precipitation. Regions of high coupling strength (hotspots) are identified and analysed focusing on the link between soil moisture-evapotranspiration coupling and soil moisture-precipitation coupling, the relation between the coupling strength and seasonal predictability and the hotspots importance for extreme precipitation events. La Plata Basin and northeastern Brazil are identified as hotspots due to evapotranspiration recycling. A region within the South Atlantic Convergence Zone is identified as a hotspot of precipitation explained by moisture advection. Extreme precipitation events are repressed in parts of La Plata Basin when the link between precipitation and soil moisture is cut through using prescribed soil moisture.Fil: Sörensson, Anna. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Menendez, Claudio Guillermo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentin

Tourism companies’ sustainability communication – creating legitimacy and value

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A Note on Soil Moisture Memory and Interactions with Surface Climate for Different Vegetation Types in the La Plata Basin

This work examines the evolution of soil moisture initialization biases and their effects on seasonal forecasts depending on the season and vegetation type for a regional model over the La Plata basin in South America. WRF–Noah simulations covering multiple cases during a 2-yr period are designed to emphasize the conceptual nature of the simulations at the expense of the statistical significance of the results. Analysis of the surface climate shows that the seasonal predictive skill is higher when the model is initialized during the wet season and the initial soil moisture differences are small. Large soil moisture biases introduce large surface temperature biases, particularly for savanna, grassland, and cropland vegetation covers at any time of the year, thus introducing uncertainty in the surface climate. Regions with evergreen broadleaf forest have roots that extend to the deep layer whose moisture content affects the surface temperature through changes in the partitioning of the surface fluxes. The uncertainties of monthly maximum temperature can reach several degrees Celsius during the dry season in cases when 1) the soil is much wetter in the reanalysis than in the WRF–Noah equilibrium soil moisture and 2) the memory of the initial value is long because of scarce rainfall and low temperatures. This study suggests that responses of the atmosphere to soil moisture initialization depend on how the initial wet and dry conditions are defined, stressing the need to take into account the characteristics of a particular region and season when defining soil moisture initialization experiments.Fil: Sörensson, Anna. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; ArgentinaFil: Berbery, Ernesto Hugo. University of Maryland; Estados Unido

Estimation of the flooded area over the pantanal, a South American floodplain, using modis data

Tropical floodplains, such as Pantanal in Central South America, are important features for land-atmosphere interactions. Schemes to account for floodplains should therefore be included in Earth System Models, but this requires observations of flooded area for validation. Satellite data is a possible solution to estimate the flooded area but it is important to evaluate the different flood detection algorithms available in order to use the most efficient for the region. This work explores different methods to estimate the flooded area from the MODIS MOD09A1 satellite surface reflectance product using spectral indexes (mNDWI, NDMI, NDMI-NDVI) to detect the presence of water. We include the traditional threshold-based methods but also some unsupervised classification methods such as the k-means and the Principal Component Analysis applied on the water-related spectral indexes. The calibration and validation of these methods are based on the hydrological knowledge of the region, coming from land surface models, river discharge observation and from previous satellite estimations of the flooded area. The NDMI index seems too sensible to the vegetation which leads to error in the estimation of the flooded area. The other methods were spatially and temporally consistent with previous studies over the Pantanal.Las llanuras aluviales tropicales, como el Pantanal en el centro de América del Sur, son características importantes para las interacciones tierra-atmósfera. Por lo tanto, los esquemas para dar cuenta de las llanuras aluviales deberían incluirse en los modelos del sistema terrestre, pero esto requiere observaciones del área inundada para su validación. Los datos satelitales son una posible solución para estimar el área inundada, pero es importante evaluar los diferentes algoritmos de detección de inundaciones disponibles para utilizar el más eficiente para la región. Este trabajo explora diferentes métodos para estimar el área inundada a partir del producto de reflectancia de superficie del satélite MODIS MOD09A1 utilizando índices espectrales (mNDWI, NDMI, NDMI-NDVI) para detectar la presencia de agua. Incluimos los métodos tradicionales basados ​​en umbrales, pero también algunos métodos de clasificación no supervisados, como las k-medias y el Análisis de Componentes Principales aplicados a los índices espectrales relacionados con el agua. La calibración y validación de estos métodos se basan en el conocimiento hidrológico de la región, proveniente de modelos de superficie terrestre, observación de caudales de ríos y de estimaciones satelitales previas del área inundada. El índice NDMI parece demasiado sensible a la vegetación, lo que induce a errores en la estimación de la superficie inundada. Los otros métodos fueron espacial y temporalmente consistentes con estudios previos sobre el Pantanal.Fil: Schrapffer, Anthony. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; ArgentinaFil: Cappelletti, Lucía María. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; ArgentinaFil: Sörensson, Anna. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; Argentin

Identificaction and monitoring of waterlogged areas in a productive region of Argentina using Landsat information

El monitoreo de áreas inundadas y anegadas es fundamental para la Pampa argentina, una extensa región de alta productividad agrícola, ganadera e industrial. Áreas dentro de la Pampa argentina han experimentado un aumento del nivel freático en los últimos años, un factor clave que contribuye al desarrollo y la persistencia de las inundaciones. El seguimiento de estos fenómenos medioambientales extremos es esencial para alcanzar objetivos a largo plazo, p. aumentar el conocimiento para modelar y predecir la ocurrencia y evolución de eventos extremos; así como para objetivos a corto plazo, p.e. para brindar a los productores opciones potenciales de manejo del campo en caso de emergencia. Por lo tanto, el objetivo principal de este trabajo es desarrollar un método sencillo para monitorear las áreas afectadas, utilizando herramientas accesibles. Se eligió como sitio de estudio una región de 160 km2 del sureste de Córdoba para la cual se dispone de datos sobre la altura del nivel freático y la precipitación diaria. Para determinar las áreas inundadas del sitio, se utilizaron 18 imágenes Landsat-8/OLI de septiembre de 2019 a abril de 2021. En cada una de estas imágenes, los píxeles se etiquetaron según tres categorías: aguas abiertas, aguas mixtas y no acuáticas mediante la aplicación de una clasificación no supervisada de los índices de agua mNDWI y NDWI. A pesar de la falta de escenas debido a la nubosidad, las escenas pueden verse afectadas, la categoría de Aguas Abiertas y, en menor grado, la categoría de Aguas Mixtas, son capaces de capturar los cambios en la altura del nivel freático debido a la precipitación.The monitoring of ooded and waterlogged areas is essential for the Argentinean Pampas, an extensive at region of high agricultural, livestock and industrial productivity. Areas within the Argentinean Pampas have experienced a rise of the water table in recent years, a key factor contributing to the development and persistence of ooding. The monitoring of these extreme environmental events is essential for long-term objectives, e.g. increasing knowledge to model and predict the occurrence and evolution of extreme events; as well as for short-term objectives, e.g. to provide producers potential field management options in a ood emergency. Therefore, the main objective of this work is to develop a simple method for monitoring affected areas, using accessible tools. A 160 km2 region of southeastern Córdoba for which data on water table height and daily precipitation are available, was chosen as a study site. To determine the ooded areas of the site, 18 Landsat-8/OLI images from September 2019 - April 2021 were used. On each of these images, pixels were labeled according to three categories: Open Water, Mixed-Water and Non-Water by applying unsupervised classification of the mNDWI and NDWI water indices. Despite the lack of scenes due to cloud cover the scenes may suffer, the Open Water category, and to a lower degree the Mixed-Water category, are able to capture the changes in the water table height due to precipitation.Fil: Cappelletti, Lucía María. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; ArgentinaFil: Schrapffer, Anthony. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; ArgentinaFil: Sörensson, Anna. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; Argentin

Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests

A variety of modelling studies have suggested tree rooting depth as a key variable to explain evapotranspiration rates, productivity and the geographical distribution of evergreen forests in tropical South America. However, none of those studies have acknowledged resource investment, timing and physical constraints of tree rooting depth within a competitive environment, undermining the ecological realism of their results. Here, we present an approach of implementing variable rooting strategies and dynamic root growth into the LPJmL4.0 (Lund-Potsdam-Jena managed Land) dynamic global vegetation model (DGVM) and apply it to tropical and sub-tropical South America under contemporary climate conditions. We show how competing rooting strategies which underlie the trade-off between above- and below-ground carbon investment lead to more realistic simulation of intra-annual productivity and evapotranspiration and consequently of forest cover and spatial biomass distribution. We find that climate and soil depth determine a spatially heterogeneous pattern of mean rooting depth and below-ground biomass across the study region. Our findings support the hypothesis that the ability of evergreen trees to adjust their rooting systems to seasonally dry climates is crucial to explaining the current dominance, productivity and evapotranspiration of evergreen forests in tropical South America.Fil: Sakschewski, Boris. Potsdam Institute for Climate Impact Research; AlemaniaFil: Von Bloh, Werner. Humboldt-Universität zu Berlin; AlemaniaFil: Drüke, Markus. Humboldt-Universität zu Berlin; AlemaniaFil: Sörensson, Anna. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Ruscica, Romina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Langerwisch, Fanny. Universitat Potsdam; AlemaniaFil: Billing, Maik. Universidade Federal de Santa Catarina; BrasilFil: Bereswill, Sarah. Universidade Estadual de Campinas; BrasilFil: Hirota, Marina. Potsdam Institute for Climate Impact Research; AlemaniaFil: Oliveira, Rafael Silva. Potsdam Institute for Climate Impact Research; AlemaniaFil: Heinke, Jens. Potsdam Institute for Climate Impact Research; AlemaniaFil: Thonicke, Kirsten. Potsdam Institute for Climate Impact Research; Alemani

Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests

A variety of modelling studies have suggested tree rooting depth as a key variable to explain evapotranspiration rates, productivity and the geographical distribution of evergreen forests in tropical South America. However, none of those studies have acknowledged resource investment, timing and physical constraints of tree rooting depth within a competitive environment, undermining the ecological realism of their results. Here, we present an approach of implementing variable rooting strategies and dynamic root growth into the LPJmL4.0 (Lund-Potsdam-Jena managed Land) dynamic global vegetation model (DGVM) and apply it to tropical and sub-tropical South America under contemporary climate conditions. We show how competing rooting strategies which underlie the trade-off between above- and below-ground carbon investment lead to more realistic simulation of intra-annual productivity and evapotranspiration and consequently of forest cover and spatial biomass distribution. We find that climate and soil depth determine a spatially heterogeneous pattern of mean rooting depth and below-ground biomass across the study region. Our findings support the hypothesis that the ability of evergreen trees to adjust their rooting systems to seasonally dry climates is crucial to explaining the current dominance, productivity and evapotranspiration of evergreen forests in tropical South America

Understanding climate change impacts on biome and plant distributions in the Andes: Challenges and opportunities

Aim: Climate change is expected to impact mountain biodiversity by shifting species ranges and the biomes they shape. The extent and regional variation in these impacts are still poorly understood, particularly in the highly biodiverse Andes. Regional syntheses of climate change impacts on vegetation are pivotal to identify and guide research priorities. Here we review current data, knowledge and uncertainties in past, present and future climate change impacts on vegetation in the Andes. Location: Andes. Taxon: Plants. Methods: We (i) conducted a literature review on Andean vegetation responses to past and contemporary climatic change, (ii) analysed future climate projections for different elevations and slope orientations at 19 Andean locations using an ensemble of model outputs from the Coupled Model Intercomparison Project 5, and (iii) calculated changes in the suitable climate envelope area of Andean biomes and compared these results to studies that used species distribution models. Results: Future climatic changes (2040–2070) are projected to be stronger at high-elevation areas in the tropical Andes (up to 4°C under RCP 8.5), while in the temperate Andes temperature increases are projected to be up to 2°C. Under this worst-case scenario, temperate deciduous forests and the grasslands/steppes from the Central and Southern Andes are predicted to show the greatest losses of suitable climatic space (30% and 17%–23%, respectively). The high vulnerability of these biomes contrasts with the low attention from researchers modelling Andean species distributions. Critical knowledge gaps include a lack of an Andean wide plant checklist, insufficient density of weather stations at high-elevation areas, a lack of high-resolution climatologies that accommodates the Andes' complex topography and climatic processes, insufficient data to model demographic and ecological processes, and low use of palaeo data for distribution modelling. Main conclusions: Climate change is likely to profoundly affect the extent and composition of Andean biomes. Temperate Andean biomes in particular are susceptible to substantial area contractions. There are, however, considerable challenges and uncertainties in modelling species and biome responses and a pressing need for a region-wide approach to address knowledge gaps and improve understanding and monitoring of climate change impacts in these globally important biomes.publishedVersio

Regional climate change: consensus, discrepancies, and ways forward

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media