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 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 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.

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

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

Accurate simulation of energy, water, and carbon fluxes exchanging between the land surface and the atmosphere is beneficial for improving terrestrial ecohydrological and climate predictions. We systematically assessed the Noah land surface model (LSM) with mutiparameterization options (Noah-MP) in simulating these fluxes and associated variations in terrestrial water storage (TWS) and snow cover fraction (SCF) against various reference products over 18 United States Geological Survey two-digital hydrological unit code regions of the continental United States (CONUS). In general, Noah-MP captures better the observed seasonal and interregional variability of net radiation, SCF, and runoff than other variables. With a dynamic vegetation model, it overestimates gross primary productivity by 40% and evapotranspiration (ET) by 22% over the whole CONUS domain; however, with a prescribed climatology of leaf area index, it greatly improves ET simulation with relative bias dropping to 4%. It accurately simulates regional TWS dynamics in most regions except those with large lakes or severely affected by irrigation and/or impoundments. Incorporating the lake water storage variations into the modeled TWS variations largely reduces the TWS simulation bias more obviously over the Great Lakes with model efficiency increasing from 0.18 to 0.76. Noah-MP simulates runoff well in most regions except an obvious overestimation (underestimation) in the Rio Grande and Lower Colorado (New England). Compared with North American Land Data Assimilation System Phase 2 (NLDAS-2) LSMs, Noah-MP shows a better ability to simulate runoff and a comparable skill in simulating R-n but a worse skill in simulating ET over most regions. This study suggests that future model developments should focus on improving the representations of vegetation dynamics, lake water storage dynamics, and human activities including irrigation and impoundments.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23165号 / 工博第4809号 / 新制||工||1752(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 立川 康人, 准教授 市川 温, 教授 田中 茂信 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM