9 research outputs found
Microwave remote sensing of snow and environment
Hemispheric snow extent and snow mass are two important parameters affecting the water cycle, carbon cycle and the radiation balance in particular at the high latitudes. In this dissertation these topics have been investigated focusing on the mapping of snow clearance day (melt-off day) and Snow Water Equivalent (SWE) by applying spaceborne microwave radiometer instruments.
New algorithms have been developed and existing ones have been further advanced. Specific attention has been paid to estimate snow in boreal forests. This work has resulted in Climate Data Records (CDRs) of snow clearance day and daily values of SWE. Data are available for the entire Northern Hemisphere covering more than three decades. The developed CDRs are relevant for climate research, for example concerning the modeling of Earth System processes. CDR on snow clearance day can be used to map the CO2 balance between the biosphere and atmosphere in the case of boreal forests, which is demonstrated in the thesis.
Further, methodologies to assess snow mass in terms of SWE for hemispherical and regional scales have been developed. The developed methodologies have also resulted in the establishment of new Near-Real-Time (NRT) satellite data services for hydrological end-use. In hydrology SWE data are used to enhance the performance of river discharge forecasts, which is highly important for hydropower industry and flood prevention activities
Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources
Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources
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Understanding past and future changes in northern Fennoscandian snow cover.
In this project, a combination of field measurements, remote sensing data and regional climate model outputs were used to study recent and projected future changes in Northern Fennoscandian snow cover. The research questions considered in this thesis are: What are the uncertainties in remote sensing and climate modelling datasets used in snow studies? How has snow cover been changing since the 1960s and how will it change over the next century, at a regional level over Northern Fennoscandia?
Field measurements were made over two field seasons in the Khibiny Mountains in Arctic Russia. This ground data was used to gain an understanding of snow cover behaviour in the Western Mountain Regions (WMR) of the Kola Peninsula and to ground-truth 500 m resolution satellite data (MODIS: Moderate Resolution Imaging Spectroradiometer) snow products. The overall root mean square error (RMSE) for both MODIS instruments was found to be less than 10 %. The ground-truthed MODIS snow product was then used with station data to analyse past changes in snow cover in the WMR over the past 16 years. Though there is high inter-annual and spatial variability in the long-term snow cover trends in the WMR, overall, the duration of the snow cover season has increased at lower elevations and decreased at higher elevations.
Field measurements and MODIS data were used in the sensitivity analysis of the Weather Research and Forecasting (WRF) regional climate model. Twelve experiments with different physics parameterisations were run over the first field season, and a statistical scores evaluation was undertaken to determine the optimised parameter setup for modelling snow in the region. Three CMIP5 (Coupled Model Intercomparison Project 5) models were used to force WRF in historical (1990 - 1999) and two future climate (2090 - 2099) emission scenarios over Northern Fennoscandia. Outputs from the historical runs were compared to data from 10 stations across Northern Fennoscandia in order to further validate WRF. WRF makes excellent temperature estimates, with a mean bias in the yearly mean temperature outputs of the runs of -1.89 °C. The precipitation outputs are less accurate with values often higher than observations, especially for extreme precipitation events (CMIP5 âensembleâ mean RMSE of 24.0 mm for 20 + mm precipitation events).
Finally, the future runs were compared to historical runs to study projected future changes in temperature, precipitation, snowfall and snow cover. The three models give a range of different future predictions for regional climate change over Northern Fennoscandia. However, all CMIP5 models agree that in both emission scenarios mean snow cover duration will be lower over 2090 to 2099 than it was between 1990 and 1999. Importantly, changes in temperature, precipitation and snowfall are all higher, and snow cover is most impacted, in the higher emission scenario. RCP 8.5 consistently sees a higher decrease in solid precipitation than RCP 4.5 at all stations, and for all models and seasons, for example. Thus, aiming to reduce greenhouse gas emissions is still crucial to reducing anthropogenic impact on Northern Fennoscandian snow.Funded by NERC PhD studentship NE/L002507/
Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
The importance of snow cover extent (SCE) has been proven to strongly link with various
natural phenomenon and human activities; consequently, monitoring snow cover is one the most
critical topics in studying and understanding the cryosphere. As snow cover can vary signiïŹcantly
within short time spans and often extends over vast areas, spaceborne remote sensing constitutes
an eïŹcient observation technique to track it continuously. However, as optical imagery is limited
by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its
ability to sense day-and-night under any cloud and weather condition. In addition to widely applied
backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image
processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric
SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under
both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information,
and local meteorological data have also been explored to aid the snow cover analysis. This review
presents an overview of existing studies and discusses the advantages, constraints, and trajectories of
the current developments
DĂ©veloppement dâun systĂšme dâassimilation de mesures satellites micro-ondes passives dans un modĂšle de neige pour la prĂ©vision hydrologique au QuĂ©bec
Dans le contexte quĂ©bĂ©cois (Est du Canada), une bonne gestion de la ressource en eau est devenue un enjeu Ă©conomique majeur et permet Ă©galement dâĂ©viter dâimportantes catastrophes naturelles lors des crues printaniĂšres. La plus grande incertitude des modĂšles de prĂ©vision hydrologique rĂ©sulte de la mĂ©connaissance de la quantitĂ© de neige au sol accumulĂ©e durant lâhiver. Pour optimiser la gestion de ses barrages hydroĂ©lectriques, l'entreprise Hydro-QuĂ©bec veut pouvoir mieux quantifier et anticiper l'apport en eau que reprĂ©sentera la fonte des neiges au printemps. Cet apport est estimĂ© Ă partir de lâĂ©quivalent en eau de la neige (âĂENâ, ou Snow Water Equivalent, âSWEâ) extrapolĂ© sur lâensemble dâun territoire. Cette Ă©tude se concentre sur la zone subarctique et borĂ©ale du QuĂ©bec (58° - 45°N) incluant les bassins hydrographiques du complexe de la Baie James et du sud du QuĂ©bec. Ces territoires reprĂ©sentent des rĂ©gions immenses et hĂ©tĂ©rogĂšnes difficiles dâaccĂšs. Le faible nombre de stations mĂ©tĂ©orologiques permanentes et de relevĂ©s nivomĂ©triques entrainent de fortes incertitudes dans lâextrapolation de lâĂ©quivalent en eau de la neige, que ce soit Ă partir de mesures au sol ou de modĂšles de neige pilotĂ©s par des forçages mĂ©tĂ©orologiques. La couverture quasi - quotidienne et globale des observations satellitaires est donc une source dâinformation au potentiel certain, mais encore peu utilisĂ©e pour ajuster les estimations de lâĂEN dans les modĂšles hydrologiques.
Utilisant les observations satellitaires micro-ondes passives (MOP) et des mesures de hauteurs de neige au sol pour ajuster les cartes de neige interpolĂ©es, le produit ĂEN GlobSnow2 est actuellement considĂ©rĂ© comme un des plus performants Ă lâĂ©chelle globale. En comparant ce produit Ă une sĂ©rie temporelle de 30 ans de donnĂ©es au sol sur lâEst du Canada (1980 â 2009, avec un total de 38 990 mesures dâĂEN), nous avons montrĂ© que sa prĂ©cision n'Ă©tait pas adaptĂ©e pour les besoins d'Hydro-QuĂ©bec, avec une erreur quadratique moyenne (RMSE) relative de l'ordre de 36%. Une partie des incertitudes provient de la non reprĂ©sentativitĂ© des mesures de hauteur de neige au sol. Ce travail de thĂšse s'est donc concentrĂ© sur l'amĂ©lioration de la prĂ©diction du couvert nival au QuĂ©bec par lâassimilation des observations satellitaires MOP sans utilisation de relevĂ©s au sol. Les observations, dĂ©crites comme des tempĂ©ratures de brillance (TB), sont fournies par les radiomĂštres AMSR-2 (Advanced Microwave Scanning Radiometer â 2) embarquĂ©s sur le satellite Jaxa (10 x 10 km2). Lâapproche dĂ©veloppĂ©e propose de coupler un modĂšle de neige (Crocus de MĂ©tĂ©o-France) avec un modĂšle de transfert radiatif (DMRT-ML du LGGE, Grenoble) pour simuler lâĂ©mission du manteau neigeux modĂ©lisĂ©. Des modĂšles de transfert radiatifs de vĂ©gĂ©tation, de sol et dâatmosphĂšre sont ajoutĂ©s et calibrĂ©s pour reprĂ©senter le signal MOP au niveau des capteurs satellitaires. Les observations MOP dâAMSR-2 sont alors assimilĂ©es en rĂ©ajustant directement les forçages atmosphĂ©riques pilotant le modĂšle de neige. Ces forçages sont dĂ©rivĂ©s du modĂšle de prĂ©vision atmosphĂ©rique canadien GEM Ă 10 km de rĂ©solution spatiale. Le systĂšme dâassimilation implĂ©mentĂ© est un filtre particulaire par rĂ©Ă©chantillonnage dâimportance. La chaĂźne de modĂšles a Ă©tĂ© calibrĂ©e et validĂ©e avec des mesures au sol de radiomĂ©trie micro-onde et des relevĂ©s continus dâĂEN et de hauteurs de neige.
Lâassimilation des TB montre d'excellents rĂ©sultats avec des observations synthĂ©tiques simulĂ©es, amĂ©liorant la RMSE sur lâĂEN de 82% comparĂ© aux simulations dâĂEN sans assimilation. Les experiences prĂ©liminaires de lâassimilation des observations satellitaires dâAMSR-2 en 11, 19 et 37 GHz (verticale polarization) montrent une amĂ©lioration significative des biais sur les ĂEN simulĂ©s sur un important jeu de donnĂ©es ponctuelles (12 stations de mesures dâĂEN continues sur 4 annĂ©es). La moyenne des biais inversĂ©s des valeurs dâĂEN moyens et maximums sont rĂ©duits respectivement de 71 % et 32 % par rapport aux simulations dâĂEN sans assimilation. Avec lâassimilation des observations dâAMSR-2 et pour les sites avec moins de 75 % de couverts forestiers, le pourcentage d'erreur relative sur lâĂEN par rapport aux observations est de 15 % (contre 20 % sans assimilation), soit une prĂ©cision significativement amĂ©liorĂ©e pour des applications hydrologiques. Ce travail ouvre de nouvelles perspectives trĂšs prometteuses pour la cartographie dâĂEN Ă des fins hydrologiques sur une base journaliĂšre
Satellite and in situ observations for advancing global Earth surface modelling: a review
In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort
Snow Properties Retrieval Using Passive Microwave Observations
Seasonal snow cover, the second-largest component of the cryosphere, is crucial in controlling the climate system, through its important role in modifying Earthâs albedo. The temporal variability of snow extent and its physical properties in the seasonal cycle also make up a significant element to the cryospheric energy balance. Thus, seasonal snowcover should be monitored not only for its climatological impacts but also for its rolein the surface-water supply, ground-water recharge, and its insolation properties at local scales. Snowpack physical properties strongly influence the emissions from the substratum, making feasible snow property retrieval by means of the surface brightness temperature observed by passive microwave sensors. Depending on the observing spatial resolution, the time series records of daily snow coverage and a snowpacks most-critical properties such as the snow depth and snow water equivalent (SWE) could be helpful in applications ranging from modeling snow variations in a small catchment to global climatologic studies. However, the challenge of including spaceborne snow water equivalent (SWE) products in operational hydrological and hydroclimate modeling applications is very demanding with limited uptake by these systems. Various causes have been attributed to this lack of up-take but most stem from insufficient SWE accuracy. The root causes of this challenge includes the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process that are caused by uncertainties with the forward emission modeling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of the whole range of retrieval methodologies can provide the clarity needed to move the thinking forward in this important field. Following a review on snow depth and SWE retrieval methods using passive microwave remote sensing observations, this research employs a forward emission model to simulate snowpacks emission and compare the results to the PM airborne observations. Airborne radiometer observations coordinated with ground-based in-situ snow measurements were acquired in the Canadian high Arctic near Eureka, NT, in April 2011. The observed brightness temperatures (Tb) at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE) due to the volume scattering of the ground emissions by the overlying snow. At a certain point, however, as SWE increases, the emission from the snowpack offsets the scattering of the sub-nivean emission. In tundra snow, the Tb slope reversal occurs at shallower snow thicknesses. While it has been postulated that the inflection point in the seasonal time series of observed Tb V 37 GHz of tundra snow is controlled by the formation of a thick wind slab layer, the simulation of this effect has yet to be confirmed. Therefore, the Dense Media Radiative Transfer Theory forMulti Layered (DMRT-ML) snowpack is used to predict the passive microwave response from airborne observations over shallow, dense, slab-layered tundra snow. The DMRT-ML was parameterized with the in-situ snow measurements using a two-layer snowpack and run in two configurations: a depth hoar and a wind slab dominated pack. Snow depth retrieval from passive microwave observations without a-priori information is a highly underdetermined system. An accurate estimate of snow depth necessitates a-priori information of snowpack properties, such as grain size, density, physical temperature and stratigraphy, and, very importantly, a minimization of this a prior information requirement. In previous studies, a Bayesian Algorithm for Snow Water Equivalent (SWE) Estimation (BASE) have been developed, which uses the Monte Carlo Markov Chain (MCMC) method to estimate SWE for taiga and alpine snow from 4-frequency ground-based radiometer Tb. In our study, BASE is used in tundra snow for datasets of 464 footprints inthe Eureka region coupled with airborne passive microwave observationsâthe same fieldstudy that forward modelling was evaluated. The algorithm searches optimum posterior probability distribution of snow properties using a cost function between physically based emission simulations and Tb observations. A two-layer snowpack based on local snow cover knowledge is assumed to simulate emission using the Dense Media Radiative Transfer-Multi Layered (DMRT-ML) model. Overall, the results of this thesis reinforce the applicability of a physics-based emission model in SWE retrievals. This research highlights the necessity to consider the two-part emission characteristics of a slab-dominated tundra snowpack and suggests performing inversion in a Bayesian framework
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