Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

A global long-term (1981–2000) land surface temperature product for NOAA AVHRR

Land surface temperature (LST) plays an important role in the research of climate change and various land surface processes. Before 2000, global LST products with relatively high temporal and spatial resolutions are scarce, despite a variety of operational satellite LST products. In this study, a global 0.05∘×0.05∘ historical LST product is generated from NOAA advanced very-high-resolution radiometer (AVHRR) data (1981–2000), which includes three data layers: (1) instantaneous LST, a product generated by integrating several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST; and (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapor content of 0.04 and 1.0 g cm−2, respectively, evaluated against the simulation dataset, the RF-SWA method has a mean bias error (MBE) of less than 0.10 K and a standard deviation (SD) of 1.10 K. To compensate for the influence of orbital drift on LST, the retrieved RF-SWA LST was normalized with an improved ODC method. The RF-SWA LST were validated with in situ LST from Surface Radiation Budget (SURFRAD) sites and water temperatures obtained from the National Data Buoy Center (NDBC). Against the in situ LST, the RF-SWA LST has a MBE of 0.03 K with a range of −1.59–2.71 K, and SD is 1.18 K with a range of 0.84–2.76 K. Since water temperature only changes slowly, the validation of ODC LST was limited to SURFRAD sites, for which the MBE is 0.54 K with a range of −1.05 to 3.01 K and SD is 3.57 K with a range of 2.34 to 3.69 K, indicating good product accuracy. As global historical datasets, the new AVHRR LST products are useful for filling the gaps in long-term LST data. Furthermore, the new LST products can be used as input to related land surface models and environmental applications. Furthermore, in support of the scientific research community, the datasets are freely available at https://doi.org/10.5281/zenodo.3934354 for RF-SWA LST (Ma et al., 2020a), https://doi.org/10.5281/zenodo.3936627 for ODC LST (Ma et al., 2020c), and https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b)

Spatial and temporal properties of precipitation uncertainty structures over tropical oceans, The

2015 Spring.Includes bibliographical references.The global distribution of precipitation has been measured from space using a series of passive microwave radiometers for over 40 years. However, our knowledge of precipitation uncertainty is still limited. While previous studies have shown that the uncertainty associated with the surface rain rate tends to vary with geographic location and season, most likely as a consequence of inappropriate and inaccurate microphysical assumptions in the forward model, the internal uncertainty structure remains largely unknown. Hence, a classification scheme is introduced, in which the overall precipitation uncertainty consists of random noise, constant biases, and region-dependent cyclic patterns. It is hypothesized that those cyclic patterns are the result of an imperfect forward model simulation of precipitation variation associated with regional atmospheric cycles. To investigate the hypothesis, differences from ten years of collocated surface rain rate measurements from TRMM Microwave Imager and Precipitation Radar are used as a proxy to characterize the precipitation uncertainty structure. The results show that the recurring uncertainty patterns over tropical ocean basins are clearly impacted by a hierarchy of regionally prominent atmospheric cycles with multiple time scales, from the diurnal cycle to multi-annual oscillation. Spectral analyses of the uncertainty time series have also confirmed the same argument. Moreover, the relative importance of major uncertainty sources varies drastically not only from one basin to another, but also with different choices of sampling resolutions. Following the classification scheme and hypothesis proposed in this study, the magnitudes of un-explained precipitation uncertainty can be reduced up to 68% and 63% over the equatorial central Pacific and eastern Atlantic, respectively

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Synthesis of Satellite Microwave Observations for Monitoring Global Land-Atmosphere CO2 Exchange

This dissertation describes the estimation, error quantification, and incorporation of land surface information from microwave satellite remote sensing for modeling global ecosystem land-atmosphere net CO2 exchange. Retrieval algorithms were developed for estimating soil moisture, surface water, surface temperature, and vegetation phenology from microwave imagery timeseries. Soil moisture retrievals were merged with model-based soil moisture estimates and incorporated into a light-use efficiency model for vegetation productivity coupled to a soil decomposition model. Results, including state and uncertainty estimates, were evaluated with a global eddy covariance flux tower network and other independent global model- and remote-sensing based products

Assimilation des données GRACE dans le modèle MESH pour l’amélioration de l'estimation de l'équivalent en eau de la neige

Abstract: Water storage changes over space and time play a major rule in the Earth’s climate system through the exchange of water and energy fluxes among the Earth’s water storage compartments and between atmosphere, continents, and oceans. In many parts of northern-latitude areas spring meltwater controls the availability of freshwater resources. With respect to terrestrial hydrologic process, snow water equivalent (SWE) is the most critical snow characteristic to hydrologists and water resource managers. The first objective of this study examined the spatiotemporal variations of terrestrial water storages and their linkages with SWE variabilities over Canada. Terrestrial water storage anomaly (TWSA) from the Gravity Recovery and Climate Experiment (GRACE), the WaterGAP Global Hydrology Model (WGHM), and the Global Land Data Assimilation System (GLDAS) were employed. SWE anomaly (SWEA) products were provided by the Global Snow Monitoring for Climate Research version 2 (GlobSnow2), Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR-E), and Canadian Meteorological Centre (CMC). The grid cell (1°×1°) and basin-averaged analyses were applied to find any possible relationship between TWSA and SWEA over the Canadian territory, from December 2002 to March 2011. Results showed that GRACE versus CMC provided the highest percentage of significant positive correlation (62.4% of the 1128 grid cells), with an average significant positive correlation coefficient of 0.5, and a maximum of 0.9. In western Canada, GRACE correlated better with multiple SWE data sets than GLDAS. Yet, over eastern Canada, mainly in the northern Québec area (~ 55ºN), GRACE provided weak or insignificant correlations with all snow products, while GLDAS appeared to be significantly correlated. For the TWSA-SWEA analysis at the basin-averaged scale, significant relationships were observed between TWSA and SWEA for most of the fifteen basins considered (53% to 80% of the basins, depending on the SWE products considered). The best results were obtained with the CMC SWE products, compared to satellite-based SWE data. Stronger relationships were found in snow-dominated basins (Rs >= 0.7), such as the Liard [root mean square error (RMSE) = 21.4 mm] and Peace Basins (RMSE = 26.76 mm). However, despite high snow accumulation in northern Québec, GRACE showed weak or insignificant correlations with SWEA, regardless of the data sources. The same behavior was observed in the western Hudson Bay Basin. In both regions, it was found that the contribution of non-SWE compartments, including wetland, surface water, as well as soil water storages has a significant impact on the variations of total storage. These components were estimated using the WGHM simulations and then subtracted from GRACE observations. The GRACE-derived SWEA correlation results showed improved relationships with three SWEA products (CMC, GlobSnow2, AMSR-E). The improvement is particularly important in the sub-basins of the Hudson Bay, where very weak and insignificant results were previously found with GRACE TWSA data. GRACE-derived SWEA showed a significant relationship with CMC data in 93% of the basins (13% more than GRACE TWSA). In general, results revealed the importance of SWE changes in association with the terrestrial water storage (TWS) variations. The second objective of this thesis investigates whether integration of remotely sensed terrestrial water storage (TWS) information, which is derived from GRACE, can improve SWE and streamflow simulations within a semi-distributed hydrology land surface model. A data assimilation (DA) framework was developed to combine TWS observations with the MESH (Modélisation Environnementale Communautaire – Surface Hydrology) model using an ensemble Kalman smoother (EnKS). This study examined the incorporation and development of the ensemble-based GRACE data assimilation framework into the MESH modeling framework for the first time. The snow-dominated Liard Basin was selected as a case study. The proposed assimilation methodology reduced bias of monthly SWE simulations at the basin scale by 17.5 % and improved unbiased root-mean-square difference (ubRMSD) by 23 %. At the grid scale, the DA method improved ubRMSD values and correlation coefficients of SWE estimates for 85 % and 97 % of the grid cells, respectively. Effects of GRACE DA on streamflow simulations were evaluated against observations from three river gauges, where it could effectively improve the simulation of high flows during snowmelt season from April to June. The influence of GRACE DA on the total flow volume and low flows was found to be variable. In general, the use of GRACE observations in the assimilation framework not only improved the simulation of SWE, but also effectively influenced the simulation of streamflow estimates.Les variations dans l'espace et le temps du stock d'eau à travers jouent un rôle important dans le système climatique de la Terre à travers l'échange des flux d'eau et d'énergie entre les compartiments du stock d’eau de la Terre, et entre l'atmosphère, les continents et les océans. Dans les régions nordiques, la fonte de la neige contrôle la disponibilité des ressources en eau. Concernant le processus hydrologique terrestre, l'équivalent en eau de la neige (SWE) est la caractéristique de neige la plus importante pour les hydrologues et les gestionnaires des ressources en eau. Le premier objectif de cette étude a examiné les variations spatio-temporelles des réservoirs terrestres d'eau et leurs liens avec les variabilités de SWE au Canada. Des anomalies de stockage d'eau terrestre (TWSA) provenant de GRACE (Gravity Recovery and Climate Experiment), du modèle hydrologique mondial WaterGAP (WGHM) et du modèle GLDAS (Global Land Data Assimilation System) ont été utilisées. Les produits du SWEA (Snow Water Equiavalent Anomaly) sont fournis par le GlobSnow2 (Global Snow Monitoring for Climate Research version 2), le AMSR-E (Advanced Microwave Scanning Radiometer‐Earth Observing System) et le Centre météorologique canadien (CMC). L'analyse par cellule de grille (1°×1°) a été appliquée pour trouver toute relation possible entre TWSA et SWEA sur le territoire canadien, de décembre 2002 à mars 2011. Les résultats montrent que GRACE par rapport à CMC a fourni le pourcentage le plus élevé de corrélation positive significative (62,4% des 1128 cellules de la grille), avec un coefficient de corrélation positif significatif moyen de 0,5 et un maximum de 0,9. Dans la partie ouest du pays, GRACE a montré un meilleur accord avec plusieurs produits SWE que GLDAS. Pourtant, dans l'est du Canada, principalement dans le nord du Québec (~ 55° N), GRACE a fourni des corrélations faibles ou insignifiantes avec tous les produits SWE, contrairement à GLDAS qui semblait être significativement corrélé. Dans le cas de l’analyse à l'échelle du bassin versant, les relations significatives ont été observées entre TWSA et SWEA pour la plupart des quinze bassins considérés (53% à 80% des bassins, selon les produits SWE considérés). Les meilleurs résultats ont été obtenus avec les produits CMC SWE, par rapport aux données SWE satellitaires. Des relations plus fortes ont été trouvées dans les bassins dominés par la neige (Rs> = 0,7), tels que le bassin versant de Liard [erreur quadratique moyenne (RMSE) = 21,4 mm] et le bassin versant de Peace (RMSE = 26,76 mm). Cependant, malgré une forte accumulation de neige dans le nord du Québec, GRACE a montré des corrélations faibles ou insignifiantes avec SWEA, peu importent les sources de données. Le même comportement a été observé dans le bassin versant ouest de la Baie d’Hudson. Dans les deux régions, il a été constaté que la contribution des compartiments non-SWE, y compris les zones humides, les eaux de surface, ainsi que les stocks d'eau du sol a un effet significatif sur les variations du stock total. Ces composantes ont été estimées à l'aide des simulations du modèle WGHM, puis soustraites des observations GRACE. Ces résultats de corrélation SWEA dérivés de GRACE ont montré une amélioration des relations avec les trois produits SWE (CMC, GlobSnow2, AMSR-E). L'amélioration est particulièrement importante dans les sous-bassins de la Baie d’Hudson, où des résultats très faibles et insignifiants avaient été précédemment trouvés avec les données GRACE TWSA. La SWEA dérivée de GRACE a montré une relation significative avec les données CMC dans 93% des bassins (13% de plus que GRACE TWSA). En somme, les résultats obtenus dans ce premier objectif ont montré le rôle important du SWE dans les variations du stock terrestre de l'eau dans la région d’étude. Le deuxième objectif de cette thèse examine si l'intégration des informations de TWS (terrestrial water storage) dérivées de GRACE (Gravity Recovery and Climate Experiment), peut améliorer les simulations du SWE et du débit d’eau dans un modèle hydrologique semi-distribué de schéma de surface. Un cadre d'assimilation de données (DA) a été développé pour combiner les observations TWS avec le modèle MESH (Modélisation Environnementale Communautaire - Hydrologie de Surface) en utilisant un ensemble Kalman Smoother (EnKS). Cette étude était la première du genre à tenter une assimilation des données GRACE dans le modèle MESH pour améliorer l’estimation du SWE. Le bassin versant de la Liard dominé par la neige a été choisi pour le site d’étude. À l’échelle du bassin versant, la méthodologie d'assimilation proposée a réduit le biais des simulations mensuelles de SWE à 17,5% et amélioré le ubRMSD (unbiased root-mean-square difference) de 23%. À l'échelle de la grille, la méthode DA a amélioré l’estimation du SWE pour les valeurs ubRMSD et les coefficients de corrélation pour 85% et 97% des cellules de la grille, respectivement. Les effets de GRACE DA sur les simulations de débit ont été évalués par rapport aux observations de trois stations des débits, où il pourrait effectivement améliorer la simulation des débits élevés pendant la saison de fonte de la neige d'avril à juin. L'influence de GRACE DA sur le volume total et les faibles débits d’eau a été trouvée variable. En général, l'utilisation des observations GRACE dans le cadre d'assimilation non seulement a amélioré la simulation de SWE, mais a également influencé efficacement la simulation des estimations de débit

Detecting Land-Atmosphere Interactions from Observations

Dolman, A.J. [Promotor]Hurk, B.J.J.M. van den [Promotor]Jeu, R.A.M. de [Copromotor

Land Ecosystems and Hydrology

The terrestrial biosphere is an integral component of the Earth Observing System (EOS) science objectives concerning climate change, hydrologic cycle change, and changes in terrestrial productivity. The fluxes o f CO2 and other greenhouse gases from the land surface influence the global circulation models directly, and changes in land cover change the land surface biophysical properties o f energy and mass exchange. Hydrologic cycle perturbations result from terrestrially-induced climate changes, and more directly from changes in land cover acting on surface hydrologic balances. Finally, both climate and hydrology jointly control biospheric productivity, the source o f food, fuel, and fiber for humankind. The role of the land system in each of these three topics is somewhat different, so this chapter is organized into the subtopics of Land-Climate, Land-Hydrology, and Land-Vegetation interactions (Figures 5.1, 5.2, and 5.3)

Assimilation de données satellitaires pour le suivi des ressources en eau dans la zone Euro-Méditerranée

Une estimation plus précise de l'état des variables des surfaces terrestres est requise afin d'améliorer notre capacité à comprendre, suivre et prévoir le cycle hydrologique terrestre dans diverses régions du monde. En particulier, les zones méditerranéennes sont souvent caractérisées par un déficit en eau du sol affectant la croissance de la végétation. Les dernières simulations du GIEC (Groupe d'Experts Intergouvernemental sur l'Evolution du Climat) indiquent qu'une augmentation de la fréquence des sécheresses et des vagues de chaleur dans la région Euro-Méditerranée est probable. Il est donc crucial d'améliorer les outils et l'utilisation des observations permettant de caractériser la dynamique des processus des surfaces terrestres de cette région. Les modèles des surfaces terrestres ou LSMs (Land Surface Models) ont été développés dans le but de représenter ces processus à diverses échelles spatiales. Ils sont habituellement forçés par des données horaires de variables atmosphériques en point de grille, telles que la température et l'humidité de l'air, le rayonnement solaire et les précipitations. Alors que les LSMs sont des outils efficaces pour suivre de façon continue les conditions de surface, ils présentent encore des défauts provoqués par les erreurs dans les données de forçages, dans les valeurs des paramètres du modèle, par l'absence de représentation de certains processus, et par la mauvaise représentation des processus dans certaines régions et certaines saisons. Il est aussi possible de suivre les conditions de surface depuis l'espace et la modélisation des variables des surfaces terrestres peut être améliorée grâce à l'intégration dynamique de ces observations dans les LSMs. La télédétection spatiale micro-ondes à basse fréquence est particulièrement utile dans le contexte du suivi de ces variables à l'échelle globale ou continentale. Elle a l'avantage de pouvoir fournir des observations par tout-temps, de jour comme de nuit. Plusieurs produits utiles pour le suivi de la végétation et du cycle hydrologique sont déjà disponibles. Ils sont issus de radars en bande C tels que ASCAT (Advanced Scatterometer) ou Sentinel-1. L'assimilation de ces données dans un LSM permet leur intégration de façon cohérente avec la représentation des processus. Les résultats obtenus à partir de l'intégration de données satellitaires fournissent une estimation de l'état des variables des surfaces terrestres qui sont généralement de meilleure qualité que les simulations sans assimilation de données et que les données satellitaires elles-mêmes. L'objectif principal de ce travail de thèse a été d'améliorer la représentation des variables des surfaces terrestres reliées aux cycles de l'eau et du carbone dans le modèle ISBA grâce à l'assimilation d'observations de rétrodiffusion radar (sigma°) provenant de l'instrument ASCAT. Un opérateur d'observation capable de représenter les sigma° ASCAT à partir de variables simulées par le modèle ISBA a été développé. Une version du WCM (water cloud model) a été mise en œuvre avec succès sur la zone Euro-Méditerranée. Les valeurs simulées ont été comparées avec les observations satellitaires. Une quantification plus détaillée de l'impact de divers facteurs sur le signal a été faite sur le sud-ouest de la France. L'étude de l'impact de la tempête Klaus sur la forêt des Landes a montré que le WCM est capable de représenter un changement brutal de biomasse de la végétation. Le WCM est peu efficace sur les zones karstiques et sur les surfaces agricoles produisant du blé. Dans ce dernier cas, le problème semble provenir d'un décalage temporel entre l'épaisseur optique micro-ondes de la végétation et l'indice de surface foliaire de la végétation. Enfin, l'assimilation directe des sigma° ASCAT a été évaluée sur le sud-ouest de la France.More accurate estimates of land surface conditions are important for enhancing our ability to understand, monitor, and predict key variables of the terrestrial water cycle in various parts of the globe. In particular, the Mediterranean area is frequently characterized by a marked impact of the soil water deficit on vegetation growth. The latest IPCC (Intergovernmental Panel on Climate Change) simulations indicate that occurrence of droughts and warm spells in the Euro-Mediterranean region are likely to increase. It is therefore crucial to improve the ways of understanding, observing and simulating the dynamics of the land surface processes in the Euro-Mediterranean region. Land surface models (LSMs) have been developed for the purpose of representing the land surface processes at various spatial scales. They are usually forced by hourly gridded atmospheric variables such as air temperature, air humidity, solar radiation, precipitation, and are used to simulate land surface states and fluxes. While LSMs can provide a continuous monitoring of land surface conditions, they still show discrepancies due to forcing and parameter errors, missing processes and inadequate model physics for particular areas or seasons. It is also possible to observe the land surface conditions from space. The modelling of land surface variables can be improved through the dynamical integration of these observations into LSMs. Remote sensing observations are particularly useful in this context because they are able to address global and continental scales. Low frequency microwave remote sensing has advantages because it can provide regular observations in all-weather conditions and at either daytime or night-time. A number of satellite-derived products relevant to the hydrological and vegetation cycles are already available from C-band radars such as the Advanced Scatterometer (ASCAT) or Sentinel-1. Assimilating these data into LSMs permits their integration in the process representation in a consistent way. The results obtained from assimilating satellites products provide land surface variables estimates that are generally superior to the model estimates or satellite observations alone. The main objective of this thesis was to improve the representation of land surface variables linked to the terrestrial water and carbon cycles in the ISBA LSM through the assimilation of ASCAT backscatter (sigma°) observations. An observation operator capable of representing the ASCAT sigma° from the ISBA simulated variables was developed. A version of the water cloud model (WCM) was successfully implemented over the Euro-Mediterranean area. The simulated values were compared with those observed from space. A more detailed quantification of the influence of various factors on the signal was made over southwestern France. Focusing on the Klaus storm event in the Landes forest, it was shown that the WCM was able to represent abrupt changes in vegetation biomass. It was also found that the WCM had shortcomings over karstic areas and over wheat croplands. It was shown that the latter was related to a discrepancy between the seasonal cycle of microwave vegetation optical depth (VOD) and leaf area index (LAI). Finally, the direct assimilation of ASCAT sigma° observations was assessed over southwestern France