326 research outputs found

    POTENTIAL CONTRASTS IN CO2 AND CH4 FLUX RESPONSE UNDER CHANGING CLIMATE CONDITIONS: A SATELLITE REMOTE SENSING DRIVEN ANALYSIS OF THE NET ECOSYSTEM CARBON BUDGET FOR ARCTIC AND BOREAL REGIONS

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    The impact of warming on the net ecosystem carbon budget (NECB) in Arctic-boreal regions remains highly uncertain. Heightened CH4 emissions from Arctic-boreal ecosystems could shift the northern NECB from an annual carbon sink further towards net carbon source. Northern wetland CH4 fluxes may be particularly sensitive to climate warming, increased soil temperatures and duration of the soil non-frozen period. Changes in northern high latitude surface hydrology will also impact the NECB, with surface and soil wetting resulting from thawing permafrost landscapes and shifts in precipitation patterns; summer drought conditions can potentially reduce vegetation productivity and land sink of atmospheric CO2 but also moderate the magnitude of CH4 increase. The first component of this work develops methods to assess seasonal variability and longer term trends in Arctic-boreal surface water inundation from satellite microwave observations, and quantifies estimate uncertainty. The second component of this work uses this information to improve understanding of impacts associated with changing environmental conditions on high latitude wetland CH4 emissions. The third component focuses on the development of a satellite remote sensing data informed Terrestrial Carbon Flux (TCF) model for northern wetland regions to quantify daily CH4 emissions and the NECB, in addition to vegetation productivity and landscape CO2 respiration loss. Finally, the fourth component of this work features further enhancement of the TCF model by improving representation of diverse tundra and boreal wetland ecosystem land cover types. A comprehensive database for tower eddy covariance CO2 and CH4 flux observations for the Arctic-boreal region was developed to support these efforts, providing an assessment of the TCF model ability to accurately quantify contemporary changes in regional terrestrial carbon sink/source strength

    Amélioration de la caractérisation de la neige et du sol arctique afin d’améliorer la prédiction de l’équivalent en eau de la neige en télédétection micro-ondes

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    Le phénomène de l’amplification arctique consiste en une augmentation plus prononcée des températures de surface dans cette région que sur le reste du globe. Ce phénomène est notamment dû à la diminution marquée du couvert nival provoquant un déséquilibre dans le bilan d’énergie de surface via une réduction généralisée de l’albédo (rétroaction positive). L’accélération du réchauffement est jusqu’à trois fois plus élevée dans ces régions. Il est donc primordial, dans un contexte de changement climatique arctique, de poursuivre et d’améliorer le suivi à grande échelle du couvert nival afin de mieux comprendre les processus gouvernant la variabilité spatio-temporelle du manteau neigeux. Plus spécifiquement, l’Équivalent en Eau de la Neige (EEN) est généralement utilisé pour quantifier deux propriétés (hauteur et densité) de la neige. Son estimation à grande échelle dans les régions éloignées tel que l’Arctique provient actuellement essentiellement de produits en micro-ondes passives satellitaires. Cependant, il existe encore beaucoup d’incertitudes sur les techniques d’assimilation de l’ÉEN par satellite et ce projet vise une réduction de l’erreur liée à l’estimation de l’ÉEN en explorant deux des principales sources de biais tels que : 1) la variabilité spatiale de l’épaisseur et des différentes couches du manteau neigeux arctique liées à la topographie et la végétation au sol influençant l’estimation de l’ÉEN; et 2) les modèles de transfert radiatif micro-ondes de la neige et du sol ne bénéficient pas actuellement d’une bonne paramétrisation en conditions arctiques, là où les erreurs liées à l’assimilation de l’ÉEN sont les plus importantes. L’objectif global est donc d’analyser les propriétés géophysiques du couvert nival en utilisant des outils de télédétection et de modélisation pour diminuer l’erreur liée à la variabilité spatiale locale dans l’estimation du ÉEN à grande échelle, tout en améliorant la compréhension des processus locaux qui affectent cette variabilité. Premièrement, une analyse haute résolution à l’aide de l’algorithme Random Forest a permis de prédire la hauteur de neige à une résolution spatiale de 10 m avec une RMSE de 8 cm (23%) et d’en apprendre davantage sur les processus de distribution de la neige en Arctique. Deuxièmement, la variabilité du manteaux neigeux arctique (hauteur et microstructure) a été incorporée dans des simulations en transfert radiatif micro-ondes de la neige et comparée au capteur satellitaire SSMIS. L’ajout de variabilité améliore la RMSE des simulations de 8K par rapport à un manteau neigeux uniforme. Finalement, une paramétrisation du sol gelé est présentée à l’aide de mesures de rugosité provenant de photogrammétrie (Structure-from-Motion). Cela a permis d’investiguer trois modèles de réflectivité micro-ondes du sol ainsi que la permittivité effective du sol gelé avec une rugosité SfM d’une précision de 0.1 mm. Ces données de rugosité SfM avec une permittivité optimisée (ε'_19 = 3.3, ε'_37 = 3.6) réduisent significativement l’erreur des températures de brillance simulées par rapport à des mesures au sols (RMSE = 3.1K, R^2 = 0.71) pour toutes les fréquences et polarisations. Cette thèse offre une caractérisation des variables de surface (neige et sol) en Arctique en transfert radiatif micro-ondes qui bénéficie aux multiples modélisations (climatiques et hydrologiques) de la cryosphère

    Assimilation of Freeze - Thaw Observations into the NASA Catchment Land Surface Model

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    The land surface freeze-thaw (F-T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, we developed an F-T assimilation algorithm for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F-T state in the GEOS-5 Catchment land surface model. The F-T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F-T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F-T observations. The assimilation of perfect (error-free) F-T observations reduced the root-mean-square errors (RMSE) of surface temperature and soil temperature by 0.206 C and 0.061 C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7 percent and 3.1 percent, respectively). For a maximum classification error (CEmax) of 10 percent in the synthetic F-T observations, the F-T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178 C and 0.036 C, respectively. For CEmax=20 percent, the F-T assimilation still reduces the RMSE of model surface temperature estimates by 0.149 C but yields no improvement over the model soil temperature estimates. The F-T assimilation scheme is being developed to exploit planned operational F-T products from the NASA Soil Moisture Active Passive (SMAP) mission

    Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

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    Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across 9 trenches) collected over two winters at Trail Valley Creek, NWT, Canada, were applied in synthetic radiative transfer experiments. This allowed robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability of total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were sub-metre for all layers. Depth hoar was consistently ~30% of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7% under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6m, depth hoar SSA estimates of ±5-10% around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ~30cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied

    Assimilation of Freeze–Thaw Observations into the NASA Catchment Land Surface Model

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    The land surface freeze–thaw (F/T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, an F/T assimilation algorithm was developed for the NASA Goddard Earth Observing System, version 5 (GEOS-5), modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F/T state in the GEOS-5 Catchment land surface model. The F/T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F/T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F/T observations. The assimilation of perfect (error free) F/T observations reduced the root-mean-square errors (RMSEs) of surface temperature and soil temperature by 0.206° and 0.061°C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7% and 3.1%, respectively). For a maximum classification error CEmax of 10% in the synthetic F/T observations, the F/T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178° and 0.036°C, respectively. For CEmax = 20%, the F/T assimilation still reduces the RMSE of model surface temperature estimates by 0.149°C but yields no improvement over the model soil temperature estimates. The F/T assimilation scheme is being developed to exploit planned F/T products from the NASA Soil Moisture Active Passive (SMAP) mission

    Leveraging Soil Moisture Assimilation in Permafrost Affected Regions

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    The transfer of water and energy fluxes between the ground and the atmosphere is influenced by soil moisture (SM), which is an important factor in land surface dynamics. Accurate representation of SM over permafrost-affected regions remains challenging. Leveraging blended SM from microwave satellites, this study examines the potential for satellite SM assimilation to enhance LSM (Land Surface Model) seasonal dynamics. The Ensemble Kalman Filter (EnKF) is used to integrate SM data across the Iya River Basin, Russia. Considering the permafrost, only the summer months (June to August) are utilized for assimilation. Field data from two sites are used to validate the study’s findings. Results show that assimilation lowers the dry bias in Noah LSM by up to 6%, which is especially noticeable in the northern regions of the Iya Basin. Comparison with in situ station data demonstrates a considerable improvement in correlation between SM after assimilation (0.94) and before assimilation (0.84). The findings also reveal a significant relationship between SM and surface energy balance.publishedVersio

    Satellite Microwave Remote Sensing of Boreal-Arctic Land Surface State and Meteorology from AMSR-E

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    High latitude regions are undergoing significant climate-related change and represent an integral component of the Earth’s climate system. Near-surface vapor pressure deficit, soil temperature, and soil moisture are essential state variables for monitoring high latitude climate and estimating the response of terrestrial ecosystems to climate change. Methods are developed and evaluated to retrieve surface soil temperature, daily maximum/minimum air temperature, and land surface wetness information from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite for eight Boreal forest and Arctic tundra biophysical monitoring sites across Alaska and northern Canada. Daily vapor pressure deficit is determined by employing AMSR-E daily maximum/minimum air temperature retrievals. The seasonal pattern of microwave emission and relative accuracy of the estimated land surface state are influenced strongly by landscape properties including the presence of open water, vegetation type and seasonal phenology, snow cover and freeze-thaw transitions. Daily maximum/minimum air temperature is retrieved with RMSEs of 2.88 K and 2.31 K, respectively. Soil temperature is retrieved with RMSE of 3.1 K. Vapor pressure deficit (VPD) is retrieved to within 427.9 Pa using thermal information from AMSR-E. AMSR-E thermal information imparted 27% of the overall error in VPD estimation with the remaining error attributable to underlying algorithm assumptions. Land surface wetness information derived from AMSR-E corresponded with soil moisture observations and simple soil moisture models at locations with tundra, grassland, and mixed -forest/cropland land covers (r = 0.49 to r = 0.76). AMSR-E 6.9 GHz land surface wetness showed little correspondence to soil moisture observation or model estimates at locations with \u3e 20% open water and \u3e 5 m2 m-2 Leaf Area Index, despite efforts to remove the impact of open water and vegetation biomass. Additional information on open water fraction and vegetation phenology derived from AMSR-E 6.9 GHz corresponds well with independent satellite observations from MODIS, Sea-Winds, and JERS-1. The techniques and interpretations of high-latitude terrestrial brightness temperature signatures presented in this investigation will likely prove useful for future passive microwave missions and ecosystem modeling

    Évaluation du potentiel de la méthode par différence de phase copolaire de l’onde radar (CPD) en bande-X pour l’extraction de l’épaisseur du couvert nival arctique

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    Les changements d'état du manteau neigeux dans le contexte de réchauffement global observé doivent être pris en compte pour améliorer notre compréhension empirique des processus régissant les interactions thermiques et radiatives au sein de la cryosphère. La variabilité spatio-temporelle du couvert nival fait en sorte que l’approche d’acquisition de données par les techniques traditionnelles, telle que la prise de mesure ponctuelle sur le terrain, ne peut répondre en totalité aux questions de recherche à l’échelle globale (Bokhorst et al., 2016). Cette variabilité est une des contraintes principales dans le développement de modèles auxquelles les micro-ondes actives (radar à ouverture synthétique - RSO) peuvent répondre en surpassant les méthodes utilisant les micro-ondes passives en termes de résolution spatiale. Le suivi à haute résolution spatiale de l'épaisseur de la neige (SD) permettrait une meilleure paramétrisation des processus locaux qui dirigent la variabilité spatiale de la neige, qui est une limitation connue pour le développement de modèles dans ces régions (Domine et al., 2018; King et al., 2018; Meloche et al., 2020). L’objectif général de l’étude est donc d’évaluer le potentiel du capteur TerraSAR-X (TSX) avec la méthode de changement de phase copolaire de l’onde (CPD) pour la caractérisation du couvert nival à haute résolution spatiale. Pour l’atteinte de cet objectif, les étapes suivantes ont été réalisées : (i) Quantifier la variabilité spatio-temporelle des propriétés géophysiques et de l’épaisseur du manteau neige dans un bassin versant arctique; (iii) Quantifier l’évolution de la neige en fonction de la couverture du sol et (iii) Corréler le signal du satellite TSX à l’épaisseur de neige en fonction des informations issues des propriétés nivales mesurée en (i) et les liens avec la couverture du sol quantifié en (ii). Cette étude a été la première à effectuer une caractérisation complète de la neige couvrant l'île Herschel, combinée à des données SAR. Grâce à l’utilisation d’une carte à haute résolution spatiale du couvert végétal disponible au projet, nous avons pu quantifier la variabilité de l’épaisseur de neige ainsi que l’index topographique d’humidité du sol (TWI). Le TWI a permis de mieux comprendre l’interaction onde-sol où un angle d'incidence élevé avec un TWI élevé (>7,0) permet d'extraire une corrélation entre l’épaisseur de neige et le CPD. Les travaux futurs devraient porter sur le développement d’un seuil de sensibilité du CPD au TWI et à l'angle d'incidence afin de cartographier l'épaisseur de la neige dans de tels environnements et d'évaluer le potentiel de l'utilisation d'outils d'interpolation pour compléter les cartes d’épaisseur de neige où l’approche par CPD n’est pas possible.Abstract : Changes in the state of the snowpack in the context of observed global warming must be considered to improve our empirical understanding of the processes governing thermal and radiative interactions within the cryosphere. The spatiotemporal variability of the snowpack means that the data acquisition approach using traditional techniques, such as point measurements in the field, cannot fully address global-scale research questions (Bokhorst et al., 2016). This variability is one of the primary constraints in model development that active microwaves (synthetic aperture radar - SAR) can address by outperforming methods using passive microwaves in terms of spatial resolution. High spatial resolution monitoring of snow depth (SD) would allow for better parameterization of local processes that drive the spatial variability of snow, which is a known limitation for model development in these regions (Domine et al., 2018; King et al., 2018; Meloche et al., 2020). The overall objective of the study is therefore to evaluate the potential of the TerraSAR-X (TSX) sensor with the wave copolar phase difference (CPD) method for characterizing snow cover at high spatial resolution. To achieve this objective, the following steps were performed: (i) Quantify the spatio-temporal variability of geophysical properties and snowpack depth in an Arctic watershed; (ii) Quantify the evolution of snow as a function of land cover; and (iii) Correlate the TSX satellite signal to snow depth based on information from snow properties measured in (i) and the links to land cover quantified in (ii). This study was the first to perform a complete characterization of the snow covering Herschel Island, combined with SAR data. Using a high spatial resolution vegetation classification available to the project, we were able to quantify the variability of snow depth as well as the topographic soil wetness index (TWI). The TWI provided a better understanding of the electromagnetic wave-ground interaction where a high incidence angle with a high TWI (>7.0) allows us to extract a correlation between snow depth and CPD. Future work should focus on developing a threshold for the sensitivity of CPD to TWI and incidence angle to map snow depth in such environments and to evaluate the potential of using interpolation tools to supplement snow depth maps where the CPD approach is not possible

    Surface water inundation in the boreal-Arctic: potential impacts on regional methane emissions

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    Northern wetlands may be vulnerable to increased carbon losses from methane (CH4), a potent greenhouse gas, under current warming trends. However, the dynamic nature of open water inundation and wetting/drying patterns may constrain regional emissions, offsetting the potential magnitude of methane release. Here we conduct a satellite data driven model investigation of the combined effects of surface warming and moisture variability on high northern latitude (>= 45 degrees N) wetland CH4 emissions, by considering (1) sub-grid scale changes in fractional water inundation (Fw) at 15 day, monthly and annual intervals using 25 km resolution satellite microwave retrievals, and (2) the impact of recent (2003-11) wetting/drying on northern CH4 emissions. The model simulations indicate mean summer contributions of 53 Tg CH4 yr(-1) from boreal-Arctic wetlands. Approximately 10% and 16% of the emissions originate from open water and landscapes with emergent vegetation, as determined from respective 15 day Fw means or maximums, and significant increases in regional CH4 efflux were observed when incorporating satellite observed inundated land fractions into the model simulations at monthly or annual time scales. The satellite Fw record reveals widespread wetting across the Arctic continuous permafrost zone, contrasting with surface drying in boreal Canada, Alaska and western Eurasia. Arctic wetting and summer warming increased wetland emissions by 0.56 Tg CH4 yr(-1) compared to the 2003-11 mean, but this was mainly offset by decreasing emissions (-0.38 Tg CH4 yr(-1)) in sub-Arctic areas experiencing surface drying or cooling. These findings underscore the importance of monitoring changes in surface moisture and temperature when assessing the vulnerability of boreal-Arctic wetlands to enhanced greenhouse gas emissions under a shifting climate
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