Comparison of parsimonious dynamic vegetation modelling approaches for semiarid climates

A large portion of Earth¿s terrestrial surface is subject to arid climatic water stress. As in these regions the hydrological cycle and the vegetation dynamics are tightly interconnected, a coupled modeling of these two systems is needed to fully reproduce the ecosystems¿ behavior over time and to predict possible future responses to climate change. In this thesis, the performance of three parsimonious dynamic vegetation models, suitable for inclusion in an operational ecohydrological model, are tested in a semi-arid Aleppo pine forest area in the south-east of Spain. The first model considered, HORAS (Quevedo & Francés, 2008), simulates growth as a function of plant transpiration (T), evaluating environmental restraints through the transpiration-reference evapotranspiration ratio. The state variable related to vegetation is R, relative foliar biomass, which is equivalent to FAO crop coefficient (Allen et al., 1998), but not fixed in time. The HORAS model was then abandoned because of its unsatisfactory results, probably due to a poor simulation of evaporation and transpiration processes. As for the other two models, WUE-model and LUE-model, the state variable is the leaf biomass (Bl, kg dry mass m-2 vegetation cover). Both models simulate gross primary production (GPP), in the first case as a function of transpiration and water use efficiency (WUE), in the second case as a function of absorbed photosynthetically active radiation (APAR) and light use efficiency (LUE). Net primary production (NPP) is then calculated taking into account respiration. The modelling is focused particularly on simulating foliar biomass, which is obtained from NPP through an allocation equation based on the maximum leaf area index (LAI) sustainable by the system, and considering turnover. An analysis of the information offered by MODIS EVI, NDVI, and LAI products was also performed, in order to investigate vegetation dynamics in the study site and to select the best indices to be used as observational verification for models. MODIS EVI is reported in literature (Huete et al., 2002) to be highly correlated with leaf biomass. In accordance with the phenological cycle timing described for the Aleppo pine in similar climates (Muñoz et al., 2003), the EVI showed maximum values in spring and minimum values in winter. Similar results were found applying the aforementioned WUE- and LUE- models to the study area. Contrasting simulated LAI with the EVI series, the correlation coefficients rWUE = 0.45 and rLUE = 0.57 were found for the WUE-model and LUE-model respectively. Concerning NDVI, its own definition links this index to the ¿greenness¿ of the target, so that it appears highly linked to chlorophyll content and vegetation condition, but only indirectly related to LAI. Photosynthetic pigment concentrations are reported to be sensitive to water stress in Aleppo pine (Baquedano and Castillo, 2006) so, to compare the models¿ results with NDVI, the simulated LAI was corrected by plant water-stress. The resulting correlation coefficients were rWUE = 0.62 and rLUE = 0.59. Lastly, MODIS LAI and ET were found to be unreliable in the study area because very low compared to field data and to values reported in literature (e.g. Molina & del Campo, 2012) for the same species in similar climatic conditions. The performance of both WUE- and LUE- models in this semi-arid region is found to be reasonable. However, the LUE-model presents the advantages of a better performance, the possibility to be used in a wider range of climates and to have been extensively tested in literature.Pasquato, M. (2013). Comparison of parsimonious dynamic vegetation modelling approaches for semiarid climates [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34326TESI

Iberian peninsula ecosystem carbon fluxes: a model-data integration study

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia do Ambiente pela Universidade Nova de Lisboa,Faculdade de Ciências e TecnologiaTerrestrial ecosystems play a key role within the context of the global carbon cycle. Characterizing and understanding ecosystem level responses and feedbacks to climate drivers is essential for diagnostic purposes as well as climate modelling projections. Consequently,numerous modelling and data driven approaches emerge, aiming the appraisal of biosphereatmosphere carbon fluxes. The combination of biogeochemical models with observations of ecosystem carbon fluxes in a model-data integration framework enables the recognition of potential limitations of modelling approaches. In this regard, the steady-state assumption represents a general approach in the initialization routines of biogeochemical models that entails limitations in the ability to simulate net ecosystem fluxes and in model development exercises. The present research addresses the generalized assumption of initial steady-state conditions in ecosystem carbon pools for modelling carbon fluxes of terrestrial ecosystems, from local to regional scales. At local scale, this study aims to evaluate the implications of equilibrium assumptions on modelling performance and on optimized parameters and uncertainty estimates based on a model-data integration approach. These results further aim to support the estimates of regional net ecosystem fluxes, following a bottom-up approach, by focusing on parameters governing net primary production (NPP) and heterotrophic respiration (RH)processes, which determine the simulation of the net ecosystem production fluxes in the CASA model. An underlying goal of the current research is addressed by focusing on Mediterranean ecosystem types, or ecosystems potentially present in Iberia, and evaluate the general ability of terrestrial biogeochemical models in estimating net ecosystem fluxes for the Iberian Peninsula region. At regional scales, and given the limited information available, the main objective is to minimize the implications of the initial conditions in the evaluation of the temporal dynamics of net ecosystem fluxes. Inverse model parameter optimizations at site level are constrained by eddy-covariance measurements of net ecosystem fluxes and driven by local observations of meteorological variables and vegetation biophysical variables from remote sensing products. Optimizations under steady-state conditions show significantly poorer model performance and higher parameter uncertainties when compared to optimizations under relaxed initial conditions. In addition, assuming initial steady-state conditions tend to bias parameter retrievals – reducing NPP sensitivity to water availability and RH responses to temperature – in order to prescribe sink conditions. But nonequilibrium conditions can be experienced in soil and/or vegetation carbon pools under alternative underlying dynamics, which are solely discernible through the integration of additional information sources, circumventing equifinality issues.Portuguese Foundation for Science and Technology (FCT),the European Union under Operational Program “Science and Innovation” (POCI 2010), PhD grant ref. SFRH/BD/6517/2001, co-sponsored by the European Social Fund. Further support,concerning the final months of the PhD, was provided by a Max Planck Society research fellowship

Modeling oil palm monoculture and its associated impacts on land-atmosphere carbon, water and energy fluxes in Indonesia

In dieser Studie wird ein neues Modul “CLM-Palm” für mehrjährige Nutzpflanzen zur Modellierung einer funktionellen Gruppe (plant functional type) für Ölpalmen im Rahmen des Community Land Models (CLM4.5) entwickelt, um die Auswirkungen der Transformation eines tropischen Waldes in eine Ölpalmenplantage auf die Kohlenstoff-, Wasser- und Energieflüsse zwischen Land und Atmosphäre zu quantifizieren. Um die Morphologie der Ölpalme möglichst detailgetreu darzustellen (das heißt, dass ungefähr 40 Phytomere einen mehrschichtigen Kronenraum formen), wird in dem Modul CLM-Palm eine phänologische und  physiologische Parametrisierung auf Skalen unterhalb des Kronraums eingeführt, so dass jedem Phytomer sein eigenes prognostisches Blattwachstum und seine Erntekapazität zugeordnet wird, während Stamm und Wurzeln gemeinsam genutzt werden. Das Modul CLM-Palm wurde ausschließlich für Ölpalmen getestet, ist aber auch für andere Palmarten (z. B. Kokospalmen) interessant.  Im ersten Kapitel dieser Arbeit werden Hintergrund und Motivation dieser Arbeit vorgestellt. In Kapitel 2 wird die Entwicklung des Haupt- bzw. Kernmodells beschrieben,  inklusive Phänologie und Allokationsfunktionen zur Simulation des Wachstums und des Ertrags der Palme PFT, wodurch die Basis zur Modellierung  der biophysikalischen und biogeochemicalischen Kreisläufe innerhalb dieser Monokultur bereitgestellt wird. Die neuen Parameter für die Phänologie und die Allokation wurden sorgfältig mit Feldmessungen des Blattflächenindexes (LAI), des Ertrags und der Nettoprimärproduktion (NPP) verschiedener Ölpalmenplantagen auf Sumatra (Indonesien) kalibriert und validiert. Die Validierung zeigte die Eignung von CLM-Palm zur adäquaten Vorhersage des mittleren Blattwachstums und Ertrags für verschiedene Standorte und repräsentiert in ausreichendem Maß die signifikante Variabilität bezüglich des Stickstoffs und Alters von Standort zu Standort.  In Kapitel 3 wird die weitere Modellentwicklung und die Implementierung eines Norman-Mehrschichtmodells für den Strahlungstransport vorgestellt, das an den  mehrschichtigen Kronenraum der Ölpalme angepasst ist. Dieses Norman-Mehrschichtmodell des Strahlungstransports zeigte im Vergleich zu dem in CLM4.5 implementierten Standardmodell (basierend auf großen Blättern) bei der Simulation der Licht-Photosynthese-Kurve leichte Verbesserungen und hat  lediglich marginale Vorteile gegenüber dem ebenfalls in CLM4.5 implementierten alternativen statistischen Mehrschichtmodell.  Dennoch liefert das Norman-Modell eine detailliertere und realistischere Repräsentation des Belaubungszustands wie etwa dem dynamischen LAI, der Blattwinkelverteilung in verschiedenen Höhen, und ein ausgewogeneres Profil der absorbierten photosynthetisch aktiven Strahlung (PAR). Die Validierung mit Hilfe der Eddy-Kovarianz Flussdaten zeigte die Stärke von CLM-Palm bei der Simulation der Kohlenstoffflüsse, offenbarte aber auch Abweichungen in der simulierten Evapotranspiration (ET), dem sensiblen und dem latenten Wärmefluss (H und LE). Eine Reihe von hydrologischen Messungen im Kronenraum wird in Kapitel 4 beschrieben. Dies beinhaltet eine Adaption des in CLM4.5 eingebauten Standardmodells für Niederschlag, Interzeption und Speicherfunktionen für die speziellen Merkmale eines Ölpalmen-Kronenraums. Die überarbeitete Hydrologie des Kronenraums behob die Probleme bei der Simulation der Wasserflüsse (ET und Transpiration im Kronenraum) und verbesserte die Energieaufteilung zwischen H und LE. Kapitel 5 dokumentiert die Implementierung eines neuen dynamischen Modells für Stickstoff (nitrogen, N) in CLM-Palm zur Verbesserung der Simulation der C- und N-Dynamik, insbesondere mit Bezug auf den N-Düngeeffekte in landwirtschaftlich genutzten Systemen. Das dynamische N-Modell durchbricht die Limitierung des Standardmodells in CLM4.5, mit fixierter C-N-Stöchiometrie und erlaubt die Variation des C:N-Verhältnisses in lebendem Gewebe in Abhängigkeit der N-Verfügbarkeit und dem N-Bedarf der Pflanze.  Eine Reihe von Tests bezüglich der Düngung zeigte beispielhaft die Vorteile des dynamischen N-Modells, wie zum Beispiel die Verbesserung des Netto-Ökosystemaustauschs (net ecosystem exchange, NEE), ein realistischeres C:N-Verhältnis im Blatt, eine verbesserte Repräsentation der Effizienz des Stickstoffeinsatzes (nitrogen-use efficiency, NUE), sowie der Effekte von Düngung auf Wachstum und Ertrag. Abschließend wird in Kapitel 6 eine Anwendungsstudie gezeigt, in der die zentralen Modellentwicklungen aus den vorangegangenen Kapiteln verwendet werden. Eine junge und eine  erntereife Ölpalmenplantage sowie ein Primärregenwald wurden simuliert und verglichen. Sie wiesen klare Unterschiede in den C-Flüssen und in den biophysikalischen Merkmalen (z.B. ET und Oberflächentemperatur) auf. Ölpalmenplantagen können durch Wachstumsentwicklung (im Alter von etwa 4 Jahren)  ebenso hohe und darüber hinausgehende C-Assimilation und Wassernutzungsraten erreichen wie Regenwälder, haben jedoch im Allgemeinen eine höhere Oberflächentemperatur als eine bewaldete Fläche – dies gilt auch für erntereife Plantagen. Eine Simulation des Übergangs, die zwei Rotationsperioden mit Neubepflanzungen alle 25 Jahre umspannt, zeigte dass der Anbau von Ölpalmen auf längeren Zeitskalen lediglich in etwa die Hälfte des ursprünglichen C-Speichers der bewaldeten Fläche vor dem Kahlschlag  rückspeichern kann. Das im Boden gespeicherte C nimmt in einer bewirtschafteten Plantage aufgrund des begrenzten Streurücklaufs langsam und graduell ab. Insgesamt reduziert die Umwandlung eines Regenwaldes in eine Ölpalmenplantage die langfristigen C-Speicher und die Kapazität der Fläche zur C-Sequestrierung und trägt potentiell zur Erwärmung der Landoberfläche bei – trotz des schnellen Wachstums und der hohen C-Assimilationsrate einer stark gedüngten Plantage. Zur Einschätzung der regionalen und globalen Effekte der Ausbreitung der Kultivierung von Ölpalmen auf die Austauschprozesse zwischen Land und Atmosphäre und auf das Klima ist es notwendig eine Upscaling-Studie durchzuführen

Climate Models

Climate models is a very broad topic, so a single volume can only offer a small sampling of relevant research activities. This volume of 14 chapters includes descriptions of a variety of modeling studies for a variety of geographic regions by an international roster of authors. The climate research community generally uses the rubric climate models to refer to organized sets of computer instructions that produce simulations of climate evolution. The code is based on physical relationships that describe the shared variability of meteorological parameters such as temperature, humidity, precipitation rate, circulation, radiation fluxes, etc. Three-dimensional climate models are integrated over time in order to compute the temporal and spatial variations of these parameters. Model domains can be global or regional and the horizontal and vertical resolutions of the computational grid vary from model to model. Considering the entire climate system requires accounting for interactions between solar insolation, atmospheric, oceanic and continental processes, the latter including land hydrology and vegetation. Model simulations may concentrate on one or more of these components, but the most sophisticated models will estimate the mutual interactions of all of these environments. Advances in computer technology have prompted investments in more complex model configurations that consider more phenomena interactions than were possible with yesterday s computers. However, not every attempt to add to the computational layers is rewarded by better model performance. Extensive research is required to test and document any advantages gained by greater sophistication in model formulation. One purpose for publishing climate model research results is to present purported advances for evaluation by the scientific community

Hillslope-scale soil moisture estimation with a physically-based ecohydrology model and L-band microwave remote sensing observations from space

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2009.Includes bibliographical references (p. 469-488).Soil moisture is a critical hydrosphere state variable that links the global water, energy, and carbon cycles. Knowledge of soil moisture at scales of individual hillslopes (10's to 100's of meters) is critical to advancing applications such as landslide prediction, rainfall-runoff modeling, and wildland fire fuel load assessment. This thesis develops a data assimilation framework that employs the ensemble Kalman Filter (EnKF) to estimate the spatial distribution of soil moisture at hillslope scales by combining uncertain model estimates with noisy active and passive L-band microwave observations. Uncertainty in the modeled soil moisture state is estimated through Monte Carlo simulations with an existing spatially distributed ecohydrology model. Application of the EnKF to estimate hillslope-scale soil moisture in a watershed critically depends on: (1) identification of factors contributing to uncertainty in soil moisture, (2) adequate representation of the sources of uncertainty in soil moisture, and (3) formulation of an observing system to estimate the geophysically observable quantities based on the modeled soil moisture. Uncertainty in the modeled soil moisture distribution arises principally from uncertainty in the hydrometeorological forcings and imperfect knowledge of the soil parameters required as input to the model. Three stochastic models are used in combination to simulate uncertain hourly hydrometeorological forcings for the model. Soil parameter sets are generated using a stochastic approach that samples low probability but potentially high consequence parameter values and preserves correlation among the parameters. The observing system recognizes the role of the model in organizing the factors effecting emission and reflection of L-band microwave energy and emphasizes the role of topography in determining the satellite viewing geometry at hillslope scales.(cont.) Experiments in which true soil moisture conditions were simulated by the model and used to produce synthetic observations at spatial scales significantly coarser than the model resolution reveal that sequential assimilation of observations improves the hillslope-scale near-surface moisture estimate. Results suggest that the data assimilation framework is an effective means of disaggregating coarse-scale observations according to the model physics represented by the ecohydrology model. The thesis concludes with a discussion of contributions, implications, and future directions of this work.by Alejandro Nicolas Flores.Ph.D