Soil moisture plays a major role in the hydrologic water balance and is the basis for most hydrological models. It influences the partitioning of energy and moisture inputs at the land surface. Because of its importance, it has been used as a key variable for many hydrological studies such as flood forecasting, drought studies and the determination of groundwater recharge. Therefore, spatially distributed soil moisture with reasonable temporal resolution is considered a valuable source of information for hydrological model parameterization and validation. Unfortunately, soil moisture is difficult to measure and remains essentially unmeasured over spatial and temporal scales needed for a number of hydrological model applications. In 2002, the Gravity Recovery And Climate Experiment (GRACE) satellite platform was launched to measure, among other things, the gravitational field of the earth. Over its life span, these orbiting satellites have produced time series of mass changes of the earth-atmosphere system. The subsequent outcome of this, after integration over a number of years, is a time series of highly refined images of the earth's mass distribution. In addition to quantifying the static distribution of mass, the month-to-month variation in the earth's gravitational field are indicative of the integrated value of the subsurface total water storage for specific catchments. Utilization of these natural changes in the earth's gravitational field entails the transformation of the derived GRACE geopotential spherical harmonic coefficients into spatially varying time series estimates of total water storage. These remotely sensed basin total water storage estimates can be routinely validated against independent estimates of total water storage from an atmospheric-based water balance approach or from well calibrated macroscale hydrologic models. The hydrological relevance and implications of remotely estimated GRACE total water storage over poorly gauged, wetland-dominated watershed as well as over a deltaic region underlain by a thick sand aquifer in Western Canada are the focus of this thesis. The domain of the first case study was the Mackenzie River Basin wherein the GRACE total water storage estimates were successfully inter-compared and validated with the atmospheric based water balance. These were then used to assess the WATCLASS hydrological model estimates of total water storage. The outcome of this inter-comparison revealed the potential application of the GRACE-based approach for the closure of the hydrological water balance of the Mackenzie River Basin as well as a dependable source of data for the calibration of traditional hydrological models. The Mackenzie River Basin result led to a second case study where the GRACE-based total water storage was validated using storage estimated from the atmospheric-based water balance P-E computations in conjunction with the measured streamflow records for the Saskatchewan River Basin at its Grand Rapids outlet in Manitoba. The fallout from this comparison was then applied to the characterization of the Prairie-wide 2002/2003 drought enabling the development of a new drought index now known as the Total Storage Deficit Index (TSDI). This study demonstrated the potential application of the GRACE-based technique as a tool for drought characterization in the Canadian Prairies. Finally, the hydroinformatic approach based on the artificial neural network (ANN) enabled the downscaling of the groundwater component from the total water storage estimate from the remote sensing satellite, GRACE. This was subsequently explored as an alternate source of calibration and validation for a hydrological modeling application over the Assiniboine Delta Aquifer in Manitoba. Interestingly, a high correlation exists between the simulated groundwater storage from the coupled hydrological model, CLM-PF and the downscaled groundwater time series storage from the remote sensing satellite GRACE over this 4,000 km2 deltaic basin in Canada.

Soil moisture plays a major role in the hydrologic water balance and is the basis for most hydrological models. It influences the partitioning of energy and moisture inputs at the land surface. Because of its importance, it has been used as a key variable for many hydrological studies such as flood forecasting, drought studies and the determination of groundwater recharge. Therefore, spatially distributed soil moisture with reasonable temporal resolution is considered a valuable source of information for hydrological model parameterization and validation. Unfortunately, soil moisture is difficult to measure and remains essentially unmeasured over spatial and temporal scales needed for a number of hydrological model applications. In 2002, the Gravity Recovery And Climate Experiment (GRACE) satellite platform was launched to measure, among other things, the gravitational field of the earth. Over its life span, these orbiting satellites have produced time series of mass changes of the earth-atmosphere system. The subsequent outcome of this, after integration over a number of years, is a time series of highly refined images of the earth's mass distribution. In addition to quantifying the static distribution of mass, the month-to-month variation in the earth's gravitational field are indicative of the integrated value of the subsurface total water storage for specific catchments. Utilization of these natural changes in the earth's gravitational field entails the transformation of the derived GRACE geopotential spherical harmonic coefficients into spatially varying time series estimates of total water storage. These remotely sensed basin total water storage estimates can be routinely validated against independent estimates of total water storage from an atmospheric-based water balance approach or from well calibrated macroscale hydrologic models. The hydrological relevance and implications of remotely estimated GRACE total water storage over poorly gauged, wetland-dominated watershed as well as over a deltaic region underlain by a thick sand aquifer in Western Canada are the focus of this thesis. The domain of the first case study was the Mackenzie River Basin wherein the GRACE total water storage estimates were successfully inter-compared and validated with the atmospheric based water balance. These were then used to assess the WATCLASS hydrological model estimates of total water storage. The outcome of this inter-comparison revealed the potential application of the GRACE-based approach for the closure of the hydrological water balance of the Mackenzie River Basin as well as a dependable source of data for the calibration of traditional hydrological models. The Mackenzie River Basin result led to a second case study where the GRACE-based total water storage was validated using storage estimated from the atmospheric-based water balance P-E computations in conjunction with the measured streamflow records for the Saskatchewan River Basin at its Grand Rapids outlet in Manitoba. The fallout from this comparison was then applied to the characterization of the Prairie-wide 2002/2003 drought enabling the development of a new drought index now known as the Total Storage Deficit Index (TSDI). This study demonstrated the potential application of the GRACE-based technique as a tool for drought characterization in the Canadian Prairies. Finally, the hydroinformatic approach based on the artificial neural network (ANN) enabled the downscaling of the groundwater component from the total water storage estimate from the remote sensing satellite, GRACE. This was subsequently explored as an alternate source of calibration and validation for a hydrological modeling application over the Assiniboine Delta Aquifer in Manitoba. Interestingly, a high correlation exists between the simulated groundwater storage from the coupled hydrological model, CLM-PF and the downscaled groundwater time series storage from the remote sensing satellite GRACE over this 4,000 km2 deltaic basin in Canada.

Aus Beobachtungsdaten der Satellitenmission GRACE (Gravity Recovery and Climate Experiment) können Variationen des Erdschwerefeldes auf großen räumlichen Skalen mit hoher Genauigkeit abgeleitet werden. Die Variationen auf zeitlichen Skalen von mehreren Tagen bis Wochen und räumlichen Skalen von wenigen hundert Kilometern sind insbesondere auf Änderungen der kontinentalen Wassermassen zurückzuführen. Die vorliegende Promotionsarbeit beschäftigt sich mit der Bestimmung hydrologischer Massenvariationen aus GRACE-Daten am Beispiel der vier größten sibirischen Flusseinzugsgebiete Ob, Jenissei, Lena und Kolyma. Darauf aufbauend sollen in Kombination mit atmosphärischen Daten der NCEP-Reanalyse Süßwassereinträge in den Arktischen Ozean abgeleitet werden. Die Süßwassereinträge beeinflussen nachhaltig den Salzgehalt und damit das ozeanographische Regime des Arktischen Ozeans, welcher wiederum einen Einfluss auf die globale thermohaline Zirkulation hat. Da die großen Strömungen des Weltozeans einen grundlegenden Faktor des globalen Klimageschehens darstellen, sind die Änderungen des Süßwassereintrages ein wichtiger Aspekt hinsichtlich prognostizierter Klimatrends. Der Abfluss kann an ausgewählten Messpunkten mit einer hohen zeitlichen Auflösung beobachtet werden. Die Datenreihen weisen jedoch immer wieder Lücken auf und die bodengebundenen Messungen sind oft schwierig und kostenintensiv. Messmethoden, die unabhängig vom Zugang ins Messgebiet sind, können einen großen Fortschritt bei der Beobachtung sich ändernder Massen und Süßwasserflüsse leisten und damit einen Beitrag für ein besseres Verständnis gekoppelter komplexer Prozesse der Arktis liefern. Da die Fehlerstruktur der GRACE-Daten komplex und bis heute nicht vollständig verstanden ist, erfolgt zunächst eine Untersuchung des GRACE-Fehlerhaushaltes. Zudem werden die Fehlereffekte aufgrund des begrenzten räumlichen Spektrums und damit einhergehender Leck-Effekte auf Ebene von Gebietsmittelwerten analysiert und Lösungsvorschläge diskutiert. Dabei sind folgende Aspekte von Bedeutung: Erweiterung der GRACE-Datenreihe um geeigente Terme ersten Grades und Abschätzung von Leck-Effekten, verursacht durch das begrenzte Spektrum der Kugelfunktionsentwicklung. Leck-Effekte aufgrund ozeanischer Signalanteile sind bzgl. der Einzugsgebiete sibirischer Flusssysteme klein (< 1%), wohingegen Leck-Effekte aufgrund kontinentaler Signalanteile je nach Gebietsgröße relative Fehler von 8-17% nach sich ziehen. Die größten Fehlereffekte resultieren jedoch aus den Koeffizienten hoher Grade. Die Filterung der GRACE-Daten ermöglicht die Glättung fehlerbehafterer Signalanteile. Neben den in der Literatur gängigen Filtern wurde im Rahmen der Arbeit ein Kombinationsfilter entwickelt, welches auf Basis von räumlichen Vorinformationen aus Hydrologiemodellen signifikante Signalstrukturen in den GRACE-Datenreihen detektiert. Somit muss lediglich ein Restsignal mittels Filterung gedämpft werden. Mit dem Kombinationsfilter können sowohl feinere Signalstrukturen als auch größere Signalamplituden auf Land erhalten werden. Im Vergleich zu reinen Filteranwendungen werden hier Gesamtsignalstärke, Amplitude und Phase des jährlichen Signals gut repräsentiert. Darauf aufbauend lassen sich, in Kombination mit atmosphärischen Daten, Abflüsse für die sibirischen Flusssysteme aus GRACE-Wasserspeichervariationen ableiten. Die Validierung der berechneten Abflüsse anhand beobachteter Abflüsse zeigt eine hohe Übereinstimmung von bis zu 83%. Eine Gegenüberstellung des berechneten Abflusses der Lena mit Wasserstandsmessungen im Mündungsbereich zeigt zudem einen Zusammenhang zwischen dem maximalen Abfluss im Frühjahr und einer Zunahme des Wasserstandes in der Laptewsee. / The satellite mission GRACE (Gravity Recovery and Climate Experiment) observes the earth's gravity field on temporal scales of a few days to several weeks and spatial scales of a few hundred kilometers with high accuracy. A large part of the variations of the gravity field originate from hydrological mass changes on the continents. The dissertation discusses the determination of hydrological mass variations from GRACE for the Siberian water systems of the rivers Ob, Yenisey, Lena and Kolyma. The mass variations from GRACE data are combined with atmospheric data of the NCEP reanalysis to calculate the freshwater fluxes in the Arctic Ocean. The freshwater fluxes strongly influences the salinity and the oceanographic regime of the Arctic Ocean. In turn, the Arctic Ocean controls the global thermohaline circulation which is very important for the global climate. Because these large currents of the ocean influence the global climate, the changes of the freshwater fluxes in the Arctic Ocean are an important factor for the global climate change. The runoff can be measured pointwise with high temporal resolution, but measurements in the high latitudes are difficulty and expensive. Independent methods to measure the mass changes in the Arctic can help to determine the freshwater fluxes on large spatial scales, and contribute to understand the coupled and complex processes of the Arctic. Until present, the complex error structure of the GRACE data are not fully understand. The dissertation examines the errors and analysizes the leakage caused by the limited spectrum of the Stokes coefficients. A proposal for a solution will be discussed. The following steps are important: Expanding the GRACE data with adequate terms of degree one; Valuation of leakage errors because of the limited spectrum. Leakage due to oceanographic signals of the Arctic Ocean are small (< 1%). Leakage errors due to signals on land produces relative errors of basin averages of 8-17%. Beyond that, the largest errors are caused by the coefficients of higher degree. Filtering is an effective method to damp the error signals. In addition to the common filters described in the literature, a filter method, called composite filter, was created. Significant structures from hydrological models can be deteceted in the GRACE data without any other filtering. Only the residual signals should be filtered by using one of the common filters. In comparison to the common filters, the composite filter represents the signal strength, the signal structures, the amplitude and the phase of the saisonal signal on the continents much better. Combining hydrological mass variations from GRACE data with atmospheric data (for example the NCEP reanalysis) the runoff of the four Siberian river systems can be calculated. The validation of the calculated runoff using observations leads to a good agreement (83% for Yenisey and Lena). Furthermore, it is possible to combine the runoff of a river system with measurements of water level and salinity in the Arctic Ocean. The high runoff of the Lena river system in spring is visible in the water level changes in the Laptev sea.