Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate-carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This thesis describes adJULES in a data assimilation framework and demonstrates its ability to improve the model-data fit using eddy-covariance measurements of gross primary productivity (GPP) and latent heat (LE) fluxes. The adJULES system is extended to have the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85% of the sites used in the study, at both the calibration and evaluation stages. The new improved parameters for JULES are presented along with the associated uncertainties for each parameter. The results of the calibrations are compared to structural changes and used in a cluster analysis in order to challenge the PFT definitions in JULES. This thesis concludes with simple sensitivity studies which assess how the calibration of JULES has affected the sensitivity of the model to CO2-induced climate change.

主催：JST/CREST，Vrije University, ALTERRA, IBPC

Uncertainty in model predictions is associated with data, parameters, and model structure. The estimation of these contributions to uncertainty is a critical issue in hydrology. Using a variety of single and multiple criterion methods for sensitivity analysis and inverse modeling, the behaviors of two state-of-the-art land surface models, the Simple Biosphere Model 3 and Noah model, are analyzed. The different algorithms used for sensitivity and inverse modeling are analyzed and compared along with the performance of the land surface models. Generalized sensitivity and variance methods are used for the sensitivity analysis, including the Multi-Objective Generalized Sensitivity Analysis, the Extended Fourier Amplitude Sensitivity Test, and the method of Sobol. The methods used for the parameter uncertainty estimation are based on Markov Chain Monte Carlo simulations with Metropolis type algorithms and include A Multi-algorithm Genetically Adaptive Multi-objective algorithm, Differential Evolution Adaptive Metropolis, the Shuffled Complex Evolution Metropolis, and the Multi-objective Shuffled Complex Evolution Metropolis algorithms. The analysis focuses on the behavior of land surface model predictions for sensible heat, latent heat, and carbon fluxes at the surface. This is done using data from hydrometeorological towers collected at several locations within the Large-Scale Biosphere Atmosphere Experiment in Amazonia domain (Amazon tropical forest) and at locations in Arizona (semiarid grass and shrub-land). The influence that the specific location exerts upon the model simulation is also analyzed. In addition, the Santarém kilometer 67 site located in the Large-Scale Biosphere Atmosphere Experiment in Amazonia domain is further analyzed by using datasets with different levels of quality control for evaluating the resulting effects on the performance of the individual models. The method of Sobol was shown to give the best estimates of sensitivity for the variance-based algorithms and tended to be conservative in terms of assigning parameter sensitivity, while the multi-objective generalized sensitivity algorithm gave a more liberal number of sensitive parameters. For the optimization, the Multi-algorithm Genetically Adaptive Multi-objective algorithm consistently resulted in the smallest overall error; however all other algorithms gave similar results. Furthermore the Simple Biosphere Model 3 provided better estimates of the latent heat and the Noah model gave better estimates of the sensible heat.

The analysis of visibility between two points on the earth's terrain is a common use of GIS software. Most commercial GIS software packages include the ability to generate a viewshed, or a map of terrain surrounding a particular location that would be visible to an observer. Viewsheds are often generated using "bare-earth" Digital Elevation Models (DEMs) derived from the process of photogrammetry. More detailed models, known as Digital Surface Models (DSMs), are often generated using Light Detection and Ranging (LIDAR) which uses an airborne laser to scan the terrain. In addition to having greater accuracy than photogrammetric DEMs, LIDAR DSMs include surface features such as buildings and trees. This project used a visibility algorithm to predict visibility between observer and target locations using both photogrammetric DEMs and LIDAR DSMs of varying resolution. A field survey of the locations was conducted to determine the accuracy of the visibility predictions and to gauge the extent to which the presence of surface features in the DSMs affected the accuracy. The use of different resolution terrain models allowed for the analysis of the relationship between accuracy and optimal grid size. Additionally, a series of visibility predictions were made using Monte Carlo methods to add random error to the terrain elevation to estimate the probability of a target's being visible. Finally, the LIDAR DSMs were used to determine the linear distance of terrain along the lines-of-sight between the observer and targets that were obscured by trees or bushes. A logistic regression was performed between that distance and the visibility of the target to determine the extent to which a greater amount of vegetation along the line-of-sight impacted the target's visibility. / Master of Science

The land surface and atmosphere interact through complex feedback loops that link energy and water cycles. Effectively characterizing these linkages is critical to modeling weather and climate extremes accurately. Seasonal variability in vegetation growth and human-driven land cover changes (LCC) can alter the biophysical properties of the land surface, which can in turn influence the water cycle. We quantified the impacts of seasonal variability in vegetation growth on land surface energy and water balances using ecosystem-scale eddy covariance and large aperture scintillometer observations. Our results indicated that the monthly precipitation and seasonal vegetation characteristics such as leaf area index, root length, and stomatal resistance are the main factors influencing ecosystem land surface energy and water balances when soil moisture and available energy are not limited. Using a regional-scale climate model, we examined the effect of LCC and irrigation on summer water cycle characteristics. Changes in biophysical properties due to LCC reducing the evapotranspiration, atmospheric moisture, and summer precipitation over the contiguous United States (CONUS). The combined effects of LCC and irrigation indicated a significant drying over the CONUS, with increased duration and decreased intensity of dry spells, and reduced duration, frequency, and intensity of wet spells. Irrigated cropland areas will become drier due to the added effect of low-precipitation wet spells and long periods (3-4% increase) of dry days, whereas rainfed croplands are characterized by intense (1-5% increase), short-duration wet spells and long periods of dry days. An analysis based on future climate change projections indicated that 3–4 °C of warming and an intensified water cycle will occur over the CONUS by the end of the 21st century. The results of this study highlighted the importance of the accurate representation of seasonal vegetation changes and LCC while forecasting present and future climate. / Doctor of Philosophy / The land surface and atmosphere interact through complex feedback loops that link energy and water cycles. Effectively characterizing these linkages is critical to accurately model weather and climate extremes. We quantified the influence of human-driven land cover change (LCC), in this case, LCC associated with irrigated agriculture, and seasonal vegetation growth on the water cycle using a regional climate model and ecosystem-level observations. Our results indicated that monthly precipitation and seasonal vegetation growth are the main factors influencing land surface energy and water balances when soil moisture and solar energy are not limited. Our results showed that irrigation-related LCC reduced summer precipitation over the contiguous United States (CONUS), with an increased number of dry days (days with less than 1 mm precipitation) and reduced hourly, daily, and summer precipitation totals. Irrigated cropland areas are becoming drier due to the combined effects of low precipitation and long dry days, whereas rainfed croplands are characterized by intense short-duration precipitation and long dry days. Climate change analyses indicate that 3–4 °C of warming and an intensified water cycle will occur over the CONUS by the end of the 21st century. The results of this study highlight the importance of the accurate representation of LCC while forecasting future climate.

This thesis focuses on practical and quantitative methods for measuring effectiveness in naval ship design. An Overall Measure of Effectiveness (OMOE) model or function is an essential prerequisite for optimization and design trade-off. This effectiveness can be limited to individual ship missions or extend to missions within a task group or larger context. A method is presented that uses the Analytic Hierarchy Process combined with Multi-Attribute Value Theory to build an Overall Measure of Effectiveness and Overall Measure of Risk function to properly rank and approximately measure the relative mission effectiveness and risk of design alternatives, using trained expert opinion to replace complex analysis tools. A validation of this method is achieved through experimentation comparing ships ranked by the method with direct ranking of the ships through war gaming scenarios. The second part of this thesis presents a mathematical ship synthesis model to be used in early concept development stages of the ship design process. Tools to simplify and introduce greater accuracy are described and developed. Response Surface Models and Design of Experiments simplify and speed up the process. Finite element codes such as MAESTRO improve the accuracy of the ship synthesis models which in turn lower costs later in the design process. A case study of an Advanced Logistics Delivery Ship (ALDV) is performed to asses the use of RSM and DOE methods to minimize computation time when using high-fidelity codes early in the naval ship design process. / Master of Science

This work introduces diagnostic methods for land surface model (LSM) evaluation that enable developers to identify structural shortcomings in model parameterizations by evaluating model 'signatures' (characteristic temporal and spatial patterns of behavior) in feature, cost-function, and parameter spaces. The ensemble-based methods allow researchers to draw conclusions about hypotheses and model realism that are independent of parameter choice. I compare the performance and physical realism of three versions of Noah LSM (a benchmark standard version [STD], a dynamic-vegetation enhanced version [DV], and a groundwater-enabled one [GW]) in simulating high-frequency near-surface states and land-to-atmosphere fluxes in-situ and over a catchment at high-resolution in the U.S. Southern Great Plains, a transition zone between humid and arid climates. Only at more humid sites do the more conceptually realistic, hydrologically enhanced LSMs (DV and GW) ameliorate biases in the estimation of root-zone moisture change and evaporative fraction. Although the improved simulations support the hypothesis that groundwater and vegetation processes shape fluxes in transition zones, further assessment of the timing and partitioning of the energy and water cycles indicates improvements to the movement of water within the soil column are needed. Distributed STD and GW underestimate the contribution of baseflow and simulate too-flashy streamflow. This work challenges common practices and assumptions in LSM development and offers researchers more stringent model evaluation methods. I show that, because of equifinality, ad-hoc evaluation using single parameter sets provides insufficient information for choosing among competing parameterizations, for addressing hypotheses under uncertainty, or for guiding model development. Posterior distributions of physically meaningful parameters differ between models and sites, and relationships between parameters themselves change. 'Plug and play' of modules and partial calibration likely introduce error and should be re-examined. Even though LSMs are 'physically based,' model parameters are effective and scale-, site- and model-dependent. Parameters are not functions of soil or vegetation type alone: they likely depend in part on climate and cannot be assumed to be transferable between sites with similar physical characteristics. By helping bridge the gap between the model identification and model development, this research contributes to the continued improvement of our understanding and modeling of environmental processes. / text

Compte tenu de la vulnérabilité de la population rurale de la région sahélienne aux aléas pluviométriques, et devant les ambitions de certains acteurs d’utiliser le levier de l’usage des terres pour contribuer à l’atténuation du changement climatique, il est important de comprendre les facteurs contribuant à la variabilité de la couverture végétale au Sahel.Une synthèse de la littérature expliquant l’évolution récente de la végétation au Sahel est donc d’abord présentée. Les études s’intéressant au paradigme qui souligne l’impact de l’usage des terres sur les précipitations en Afrique de l’Ouest évaluent principalement ces effets par le couplage de modèles dynamiques globaux de végétation – DGVM – avec des modèles de circulation générale. C’est à l’amélioration d’un tel DGVM, ORCHIDEE, développé à l’Institut Pierre Simon Laplace, que le reste du travail cherche à contribuer.Comme d’autres études ont montré qu’il était possible d’utiliser en première approximation les steppes pâturées et les jachères pour décrire le comportement global de la surface sahélienne, les écarts entre modèle et mesures sont caractérisés pour une jachère située à proximité de Wankama (Niger). Plus précisément, les forces et faiblesses de la paramétrisation et de la structure par défaut du modèle sont diagnostiqués, et l’importance de la réduction d’erreur permise par l’optimisation de certains des paramètres est donnée. En particulier, l’emploi d’une résolution aux différences finies de la diffusion de l’eau dans la colonne de sol est évalué, dans la mesure où cela permet de mieux simuler la réponse rapide du flux évaporatoire aux événements pluvieux que le schéma conceptuel utilisé par défaut dans ORCHIDEE.Le réalisme du modèle est également mesuré à l’échelle régionale, par la comparaison d’observations de NDVI GIMMS_3G à la couverture végétale simulée par le modèle en réponse à différents forçages climatiques . Si les modifications introduites au cours du travail ne permettent pas de mieux décrire les tendances de la végétation au cours des dernières décennies, tirer partie des leçons du présent travail pourra se révéler utile. Il en est de même des conclusions de l’étude de la transitivité des biais conditionnels du modèle réalisée avec Tao Wang et présentée en annexe B. / The evolution of the land-surface conditions is often assessed through the use of “dynamic global vegetation models”, as is shown in a review of the current understanding of the factors of variability and of the recent evolution of the vegetation cover in the Sahel. Such models are also coupled to atmospheric general circulation models to evaluate the land feedback on precipitation in monsoonal climates.Thus, the improvement of the skills of such surface models to simulate the radiative and turbulent fluxes between the land of surface and the atmosphere in the Sahel over a range of scales from hourly to multi-annual has a potential to have significant implications. This is especially true considering the vulnerability of the rural population of the region, which largely relies on rainfed agriculture and the interest on the evolution of the carbon stocks of ecosystems in the context of climate change. Such a work on the ORCHIDEE model is presented here. In complement to croplands, rangelands and fallows represent a large share of the sahelian landscapes and have intermediate characteristics between erosion glacis and acacia bushes. As such, their evolution (in terms of albedo, roughness length,…) may be used to study the Sahel ecosystem behaviour as a first approximation. Differences between model outputs and field observations are quantified for a fallow close to Wankama (Niger). More precisely, some of the drawbacks of the standard parametrisation and structure of the model are diagnosed, and the range of reduction of the model-observation mismatch that results from optimizing some of the parameters are given (plant phenology,…). In particular, the use of a finite difference resolution of the soil water diffusion is considered as it enables to better simulate the fast response of evaporative fluxes to rainfall than the conceptual scheme routinely used in ORCHIDEE. The benefits of the use of such a “physical” hydrological scheme on the different outputs of the surface scheme is evaluated.The realism of the model is also measured at the regional scale, through a comparison with GIMMS_3G NDVI time series over West Africa. If the modifications that have been introduced in the model don’t improve its ability to describe the vegetation cover trends over the last decades in the region, several lessons can be kept from the analysis that has been realised, especially from the work on the transitivity of state-dependant model biases that has been conducted with Tao Wang and which is presented in annex B.

In this work minimally supported D-optimal designs for response surface models with spatially correlated errors are studied. The spatially correlated errors describe the correlation between two measurements depending on their distance d through the covariance function C(d)=exp(-rd). In one dimensional design space, the minimally supported D-optimal designs for polynomial models with spatially correlated errors include two end points and are symmetric to the center of the design region. Exact solutions for simple linear and quadratic regression models are presented. For models with third or higher order, numerical solutions are given. While in two dimensional design space, the minimally supported D-optimal designs are invariant under translation¡Brotation and reflection. Numerical results show that a regular triangle on the experimental region of a circle is a minimally supported D-optimal design for the first-order response surface model.

主催：JST/CREST，Vrije University, ALTERRA, IBPC