A geada é um fenômeno meteorológico adverso que causa perdas severas ao setor agrícola, em especial no Sul do Brasil. A baixa distribuição espacial da rede de estações meteorológicas dificulta o monitoramento e a previsão do fenômeno. O propósito principal desta tese foi o desenvolvimento de um modelo matemático para quantificar de forma direta e simples a probabilidade do risco de ocorrência de geada tendo como base em dados de fácil obtenção como altitude, latitude, continentalidade e temperatura do ar. O modelo criado, denominado Risco Geral de Geada (RGG), foi idealizado a partir de dois riscos básicos, o risco geográfico e o risco advindo da temperatura mínima do local, ambos com o mesma contribuição para a ocorrência de geada. O risco geográfico de geada (RGeo) foi obtido a partir do somatório dos riscos atribuídos aos fatores geográficos altitude, latitude e continentalidade. Cada um destes fatores contribui de forma diferente para a formação da geada e suas contribuições foram estimadas a partir de um modelo de regressão linear múltipla para a estimativa da temperatura mínima do ar climatológica de inverno no Rio Grande do Sul. No risco de geada associado à temperatura mínima (RTmín) foi feita a atribuição de riscos em um intervalo de temperatura entre 0 e 6°C. O modelo final obtido, válido apenas para o Rio Grande do Sul, foi testado com dados coletados em estações meteorológicas de superfície da mesorregião do Sudeste Rio-grandense, localizadas em Encruzilhada do Sul, Rio Grande e Santa Vitória do Palmar. Dados de temperatura mínima do ar nos meses de junho, julho e agosto, coletados entre os anos de 1961 e 2015, comprovaram a utilidade do modelo RGG para a definição da probabilidade do risco de ocorrência de geada, mesmo diante de incertezas atribuídas a outros fatores não descritos no modelo. Na região de teste também foi avaliada a qualidade de dados orbitais de temperatura da superfície terrestre (TST), obtidos do produto MDY11A1 da passagem noturna do sensor MODIS/AQUA, na detecção de temperaturas baixas relacionadas com a ocorrência de geada. A frequência de dias com TST inferiores a 3°C mostrou coerência tanto com os dados observados em estação meteorológica, quanto com o risco determinado pelo RGG. A coerência entre os resultados obtidos do modelo RGG e os dados reais observados em superfície e obtidos por satélite torna o modelo útil na descrição da probabilidade do risco de ocorrência de geada sobre o Rio Grande do Sul. / Frost is an adverse meteorological phenomenon that causes severe losses to the agricultural sector, especially in Southern Brazil. Low spatial distribution of the network of meteorological stations hinders monitoring and forecast phenomenon. The main purpose of this thesis was to develop a mathematical model to measure directly and easily the probability of the risk of frost based on readily available data such as altitude, latitude, continentality and air temperature. The model, called Frost General Risk (RGG), was designed from two basic risk, geographic risk and risk arising out of the local minimum temperature, both with the same contribution to the occurrence of frost. The geographical risk of frost (RGeo) was obtained from the sum of the risks attributed to geographical factors altitude, latitude and continental influence. Each of these factors contributes differently to the formation of frost and their contributions were estimated from a multiple linear regression model to estimate the minimum air temperature winter climatological in Rio Grande do Sul. In the frost risk associated with minimum temperature (RTmín) assigning risk was taken in a temperature range between 0 and 6° C. The final model obtained, valid only for the Rio Grande do Sul, has been tested with data collected from weather stations surface of the middle region of Sudeste Rio-grandense, located in Encruzilhada do Sul, Rio Grande and Santa Vitória do Palmar. Minimum temperature, the air in the months of june, july and august, collected between 1961 and 2015, have proved the usefulness of the model RGG to define the probability of the risk of frost, even in the face of uncertainty attributed to other factors not described in the model. In the test region was also evaluated the quality of satellite data of the land surface temperature (LST), the product obtained MDY11A1 the night passage of MODIS / AQUA sensor to detect low temperatures related to the occurrence of frost. The frequency of days with LST below 3° C showed much consistency with the observed data in weather station, and with the particular risk for the RGG. Consistency between the results obtained from the RGG model and the actual data observed in surface and from satellites makes the model useful in describing the probability of the risk of frost on the Rio Grande do Sul.

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.

In semiarid regions, where water resources are limited and precipitation dynamics are changing, understanding land surface-atmosphere interactions that regulate the coupled soil moisture-precipitation system is key for resource management and planning. We present a modeling approach to study soil moisture and albedo controls on planetary boundary layer height (PBLh). We used Santa Rita Creosote Ameriflux and Tucson Airport atmospheric sounding data to generate empirical relationships between soil moisture, albedo, and PBLh. Empirical relationships showed that similar to 50% of the variation in PBLh can be explained by soil moisture and albedo with additional knowledge gained by dividing the soil profile into two layers. Therefore, we coupled these empirical relationships with soil moisture estimated using a two-layer bucket approach to model PBLh under six precipitation scenarios. Overall we observed that decreases in precipitation tend to limit the recovery of the PBL at the end of the wet season. However, increases in winter precipitation despite decreases in summer precipitation may provide opportunities for positive feedbacks that may further generate more winter precipitation. Our results highlight that the response of soil moisture, albedo, and the PBLh will depend not only on changes in annual precipitation, but also on the frequency and intensity of this change. We argue that because albedo and soil moisture data are readily available at multiple temporal and spatial scales, developing empirical relationships that can be used in land surface-atmosphere applications have great potential for exploring the consequences of climate change.

Vegetation phenology controls seasonal variation in ecosystem processes and exerts important controls on land-atmosphere exchanges of carbon, water, and energy. However, the ecological processes and interactions between climate and vegetation that control phenology and associated feedbacks to the atmosphere are not fully understood. In this dissertation, I use remote sensing in combination with climate and ecological data to improve understanding of biophysical controls and feedbacks between vegetation phenology and the atmosphere in temperate forest ecosystems of North America. In the first part of this dissertation, I evaluate the agreement and characterize the similarities and differences between land surface phenology products from two remote sensing instruments (MODIS and VIIRS) that are designed to provide long-term continuity of land surface phenology measurements at global scale. Results from this analysis indicate that the VIIRS land surface phenology product provides excellent continuity with the MODIS record despite subtle differences between each instrument and the algorithms used to generate each product. In the second part of this dissertation, a state-space Bayesian modeling framework is applied to seventeen years of MODIS and daily weather data to improve understanding of what controls the timing of springtime phenology in deciduous forests of temperate and boreal North America. Results show that photoperiod is more important in warmer regions than in colder regions, which contradicts a widely held hypothesis that photoperiod provides a key safety mechanism preventing early leaf-out during springtime. In the final part of this dissertation, I use a physically-based attribution method to quantify the relative importance of covarying surface biophysical and atmospheric variables in modifying the surface energy balance during springtime. Results show that the widely observed decrease in the Bowen ratio that occurs with leaf emergence is not solely attributable to changes in surface resistance caused by increasing leaf area during spring. Rather, observed changes in the Bowen ratio reflect the combined effects of changes in surface properties and atmospheric conditions. The results from this dissertation provide an improved foundation for long-term studies focused on observing and modeling springtime vegetation phenology and associated feedbacks to the atmosphere in deciduous forest ecosystems at local-to-continental scales.

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

A land-surface module to couple a meteorological and a hydrologic model is described. lt was implemented and tested in the Leipzig\''s version of GESIMA. Preliminary results of a coupling with NASMO are presented, although this article mainly focuses on the description of the module and its effect on the atmospheric water cycle. One positive impact of the module is that it allows to produce subgrid-scale evapotranspiration in more details and to heterogenize precipitation. This strongly affects soil wetness, cloudiness and the thermal regime of the atmospheric boundary layer. / Ein Bodenmodul zur Kopplung eines meteorologischen mit einem hydrologischen Modell wird vorgestellt. Er wurde implementiert und getestet in der Leipziger Version von GESIMA. Obgleich der Schwerpunkt des Artikels auf der Beschreibung des Moduls und seiner Auswirkung auf den atmosphärischen Wasserkreislauf liegt, werden auch vorläufige Ergebnisse einer Kopplung mit NASMO präsentiert. Ein positiver Effekt des Moduls ist, daß er ermöglicht, detaillierter die subskalige Evapotranspiration zu beschreiben und den Niederschlag zu heterogenisieren. Dies wirkt sich stark auf die Bodenfeuchte, die Bewölkung und das thermische Regime der atmosphärischen Grenzschicht aus.

Modem measurement methods ofthe surface turbulent fluxes (STF) of heat, moisture and momentum in the near surface atmospheric layer by the eddy correlation method and their calculation, relay on the validity of the similarity theory of Monin-Obukhov, which requests stationarity and horizontal homogeneity. Experimental data taken at specially selected sites allowed to develop this concept. Recently performed experiments, purposely conducted in non-ideal conditions showed an underestimation ofthe STF values. To systematise this effect it is suggested to parameterize such underestimation as the influence of inhomogeneity and nonstationarity of the landscape and the atmosphere around the point of observation. This scheme might prove to be useful for the design of new validation experiments in non-ideal terrain. / Modeme Meßmethoden zur Erfassung der turbulenten Oberflächenflüsse für fühlbare und latente Wärme sowie Impuls mit Hilfe der Eddy-Korrelations-Methode basieren für die bodennahe Grenzschicht auf der Monin-Obukhov-Turbulenztheorie, die stationäre und horizontal homogene Verhältnisse voraussetzt. Über speziell ausgewählten Oberflächen wurde dieses Konzept häufig mit Erfolg überprüft. Experimente jedoch, die gezielt unter inhomogenen Verhältnissen durchgeführt werden, zeigen oft eine Unterschätzung der turbulenten Oberflächenflüsse. Es wird vorgeschlagen, diese Unterschätzungen als einen Einfluß inhomogener Umbegungsbedingungen und instationärer atmosphärischer Prozesse zu interpretieren und zu systematisieren. Dieses Schema kann dazu beitragen, eine neue Art von Validierungsexperimenten unter natürlichen Verhältnissen einer inhomogenen Umgebung zu entwerfen.

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Vegetation controls the exchange of heat, mass and momentum between the land surface and the atmosphere, and is also the primary producer that sustains life on Earth. We combine theoretical analyses, satellite and in-situ observations, and Earth system model simulations in this dissertation to illustrate the key role of vegetation in the climate system and human society. Specifically, this is accomplished via three studies, described below. First, we address the problem of how to retrieve Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FPAR) from a novel satellite Bidirectional Reflectance Factor product derived from the Multi-Angle Implementation of Atmospheric Correction algorithm. The LAI/FPAR retrieval is done via a radiative transfer model using the recently developed theory of spectral invariants. Our analyses show that the LAI/FPAR data sets developed in this study have higher accuracy and better stability relative to the existing products, especially in cloudy conditions and under high aerosol loadings. Second, we analyze the long-term trend in LAI derived from the Moderate Resolution Imaging Spectroradiometer observations and identify its main driver. We find that over a third of the terrestrial vegetation shows statistically significant increasing trends in LAI (i.e., Earth greening) during the 21st century. Both remote sensing and inventory data show that land-use management is the key driver of this greening, arising primarily from large-scale tree planting and intensive agriculture in emerging countries like China and India. This finding highlights the need for a more realistic representation of land-use practices in Earth system models. Third, we use a new method based on the concept of “two-resistances” and the Community Land Model (CLM5) runs with prescribed satellite-derived LAI to quantify the impacts of Earth greening on land surface temperature (LST). We find that over 90% of the Earth greening can lead to a local cooling effect at the annual scale. Further attribution analysis with multiple data sources reveals that aerodynamic resistance is the dominant factor controlling the LST change. The greening produces a decrease in aerodynamic resistance, which favors increased heat dissipation by turbulent fluxes, including the latent heat flux. These studies that span LAI data production, long-term trends and their impacts highlight the importance of vegetation dynamics in the natural and human systems.

Water is undeniably among the most important natural resources and the most critical in semi-arid regions like the Intermountain West of the United States. Such regions are characterized by low precipitation, the majority of which is transferred to the atmosphere from the soil and vegetation as evapotranspiration (ET). Quantification of ET is thus crucial for understanding the balance of water within the region, which is important for efficiently planning the available water resources. This study was motivated towards advancing the estimation of actual ET (ETA) in mountain ecosystems, where the variation in different types of vegetation and non-uniformity of soil including considerable stone content creates challenges for estimating water use as ET. With the aim of addressing the effect of stone content in controlling soil moisture and ET, this study examined the influence of stone content on bulk soil hydraulic properties. An averaging model referred to as a binary mixing model was used to describe the way in which water is held and released in stony soil. This approach was based on the individual hydraulic behavior of the background soil and of the stones within the soil. The effect of soil stone content on ETA was evaluated by accounting for the water retention properties of stones in the soil using a numerical simulation model (HYDRUS-1D). The results revealed overestimation of simulated ETA when effects of stone content were not accounted for in comparison to ETA measured by the state-of-the-art “eddy covariance” measurement method for ETA. An even larger-scale model was evaluated, named the Noah-Multiphysics (Noah-MP) land surface model. The land surface model was run using different arrangements of complexity to determine the importance of stone content information on simulation results. The version of the model with information about stone content along with detailed soil properties was able to provide the best Noah-MP prediction of ET. The study suggests that improvement in representation of soil properties including stone content information, can substantially advance the ability of numerical and land surface models to more accurately simulate soil water flow and ETA.