[1] We analyzed seasonal and interannual variability in hydrological fluxes and inundation dynamics of a large floodplain unit (2440 km2) along the lower Amazon River over a period of 15 years (1995–2010). Floodplain inundation was simulated using LISFLOOD-FP, which combines one-dimensional river routing with two-dimensional overland flow, and a local hydrological model. Dominant sources of inflow varied seasonally among direct rain and local runoff (November), Amazon River (December to August) and seepage (September and October). Shifts in timing of dominance among the water balance components occurred conform variations in annual peak stage. The period of dominance of river inflow over total floodplain influxes began about 1 month earlier and ended 1 month later in the 2009 high flood year compared to the 1998 low flood year. On average, river to floodplain discharge represented 0.75% of the Amazon River discharge at Obidos and 82% of the annual hydrological influxes to the floodplain. We observed an up to ninefold variation in river-floodplain annual discharge. Relatively small increments in main stem peak discharge cause disproportionately large changes in the flow routed through the floodplain. Despite the higher frequency of years with lower minimum stages, the intensification of the hydrological cycle of the Amazon Basin is causing substantially greater amounts of riverine water to flow across floodplain environments.

With continued threat from climate change and human impacts, high-resolution and continuous hydrologic data accessibility has a paramount importance for predicting trends and availability of water resources. This study presents a novel machine learning (ML)-based downscaling algorithm that produces a high spatial resolution groundwater level anomaly (GWLA) from the Gravity Recovery and Climate Experiment (GRACE) data by utilizing the relationship between Terrestrial Water Storage Anomaly (TWSA) from GRACE and other land surface and hydro-climatic variables (e.g., vegetation coverage, land surface temperature, precipitation, streamflow, and in-situ groundwater level data). The predicted downscaled GWLA data were tested using monthly in-situ groundwater level observations. Of the 32 groundwater monitoring wells available in the study site, 21 wells were used to develop the ML-based downscaling model, while the remaining 11 wells were used to assess the performance of the ML-based downscaling model. The test results showed that the model satisfactorily reproduces the spatial and temporal variation of the GWLA in the area, with acceptable correlation coefficient and Nash-Sutcliffe Efficiency values of ~0.76 and ~0.45, respectively. GRACE TWSA was the most influential predictor variable in the models, followed by stream discharge and soil moisture storage. Though model limitations and uncertainty could exist due to high spatial heterogeneity of the geologic materials and omission of human impact (e.g., abstraction), the significance of the result is undeniable, particularly in areas where in-situ well measurements are sparse.

We present a new stochastic filter technique for statistically rigorous separation of gravity signals and correlated “stripe” noises in a series of monthly gravitational spherical harmonic coefficients (SHCs) produced by the Gravity Recovery and Climate Experiment (GRACE) satellite mission. Unlike the standard destriping process that removes the stripe contamination empirically, the stochastic approach simultaneously estimates gravity signals and correlated noises relying on covariance information that reflects both the spatial spectral features and temporal correlations among them. A major benefit of the technique is that by estimating the stripe noise in a Bayesian framework, we are able to propagate statistically rigorous covariances for the destriped GRACE SHCs, i.e. incorporating the impact of the destriping on the SHC uncertainties. The Bayesian approach yields a natural resolution for the gravity signal that reflects the correlated stripe noise, and thus achieve a kind of spatial smoothing in and of itself. No spatial Gaussian smoothing is formally required although it might be useful for some circumstances. Using the stochastic filter, we process a decade-length series of GRACE monthly gravity solutions, and compare the results with GRACE Tellus data products that are processed using the “standard” destriping procedure. The results show that the stochastic filter is able to remove the correlated stripe noise to a remarkable degree even without an explicit smoothing step. The estimates from the stochastic filter for each destriped GRACE field are suitable for Bayesian integration of GRACE with other geodetic measurements and models, and the statistically rigorous estimation of the time-varying rates and seasonal cycles in GRACE time series.

We investigate terrestrial water storage (TWS) changes over the Sichuan Basin and the related impacts of water variations in the adjacent basins from GRACE (Gravity Recovery and Climate Experiment), in situ river level, and precipitation data. Although GRACE shows water increased over the Sichuan Basin from January 2003 to February 2015, two heavy droughts in 2006 and 2011 have resulted in significant water deficits. Correlations of 0.74 and 0.56 were found between TWS and mean river level/precipitation within the Sichuan Basin, respectively, indicating that the Sichuan Basin TWS is influenced by both of the local rainfall and water recharge from the adjacent rivers. Moreover, water sources from the neighboring basins showed different impacts on water deficits observed by GRACE during the two severe droughts in the region. This provides valuable information for regional water management in response to serious dry conditions. Additionally, the Sichuan Basin TWS is shown to be influenced more by the Indian Ocean Dipole (IOD) than the El Nino-Southern Oscillation (ENSO), especially for the January 2003–July 2012 period with a correlation of −0.66. However, a strong positive correlation of 0.84 was found between TWS and ENSO after August 2012, which is a puzzle that needs further investigation. This study shows that the combination of other hydrological variables can provide beneficial applications of GRACE in inter-basin areas.

Tropical forests exchange large amounts of water and energy with the atmosphere and are important in controlling regional and global climate; however, climate and evaportranspiration (E) vary significantly across multiple time scales. To better understand temporal patterns in E and climate, we measured the energy balance and meteorology of a semi-deciduous forest in the rainforest-savanna ecotone of northern Mato Grosso, Brazil, over a 7-year period and analyzed regional climate patterns over a 16-year period. Spectral analysis revealed that E and local climate exhibited consistent cycles over annual, seasonal, and weekly time scales. Annual and seasonal cycles were also apparent in the regional monthly rainfall and humidity time series, and a cycle on the order of 3–5.5 years was also apparent in the regional air temperature time series, which is coincident with the average return interval of El Niño. Annual rates of E were significantly affected by the 2002 El Niño. Prior to this event, annual E was on average 1,011 mm/year and accounted for 52 % of the annual rainfall, while after, annual E was 931 mm/year and accounted for 42 % of the annual rainfall. Our data also suggest that E declined significantly over the 7-year study period while air temperature significantly increased, which was coincident with a long-term, regional warming and drying trend. These results suggest that drought and warming induced by El Niño and/or climate change cause declines in E for semi-deciduous forests of the southeast Amazon Basin.

Tropical forests and coral reefs host a disproportionately large share of global biodiversity and provide ecosystem functions and services used by millions of people. Yet, ongoing climate change is leading to an increase in frequency and magnitude of extreme climatic events in the tropics, which, in combination with other local human disturbances, is leading to unprecedented negative ecological consequences for tropical forests and coral reefs. Here, we provide an overview of how and where climate extremes are affecting the most biodiverse ecosystems on Earth and summarize how interactions between global, regional and local stressors are affecting tropical forest and coral reef systems through impacts on biodiversity and ecosystem resilience. We also discuss some key challenges and opportunities to promote mitigation and adaptation to a changing climate at local and global scales. This article is part of the theme issue ‘Climate change and ecosystems: threats, opportunities and solutions'.

Time series of regional 2° × 2° Gravity Recovery and Climate Experiment (GRACE) solutions of surface water mass change have been computed over Africa from 2003 to 2012 with a 10-day resolution by using a new regional approach. These regional maps are used to describe and quantify water mass change. The contribution of African hydrology to actual sea level rise is negative and small in magnitude (i.e., −0.1 mm/y of equivalent sea level (ESL)) mainly explained by the water retained in the Zambezi River basin. Analysis of the regional water mass maps is used to distinguish different zones of important water mass variations, with the exception of the dominant seasonal cycle of the African monsoon in the Sahel and Central Africa. The analysis of the regional solutions reveals the accumulation in the Okavango swamp and South Niger. It confirms the continuous depletion of water in the North Sahara aquifer at the rate of −2.3 km3/y, with a decrease in early 2008. Synergistic use of altimetry-based lake water volume with total water storage (TWS) from GRACE permits a continuous monitoring of sub-surface water storage for large lake drainage areas. These different applications demonstrate the potential of the GRACE mission for the management of water resources at the regional scale.

Throughout the past decade, the Gravity Recovery and Climate Experiment (GRACE) has given an unprecedented view on global variations in terrestrial water storage. While an increasing number of case studies have provided a rich overview on regional analyses, a global assessment on the dominant features of GRACE variability is still lacking. To address this, we survey key features of temporal variability in the GRACE record by decomposing gridded time series of monthly equivalent water height into linear trends, inter-annual, seasonal, and subseasonal (intra-annual) components. We provide an overview of the relative importance and spatial distribution of these components globally. A correlation analysis with precipitation and temperature reveals that both the inter-annual and subseasonal anomalies are tightly related to fluctuations in the atmospheric forcing. As a novelty, we show that for large regions of the world high-frequency anomalies in the monthly GRACE signal, which have been partly interpreted as noise, can be statistically reconstructed from daily precipitation once an adequate averaging filter is applied. This filter integrates the temporally decaying contribution of precipitation to the storage changes in any given month, including earlier precipitation. Finally, we also survey extreme dry anomalies in the GRACE record and relate them to documented drought events. This global assessment sets regional studies in a broader context and reveals phenomena that had not been documented so far.

This study examines how the interannual variability of rainfall impacts the land water storage in the Amazon basin during the 2003-2010 time span at monthly time-scale using respectively, Tropical Rainfall Measuring Mission (TRMM) and Gravity Recovery And Climate Experiment (GRACE) satellite observations. Monthly estimates of GRACE-based terrestrial water storage (TWS) are compared to (1) TRMM rainfall, (2) in situ discharges at the outlet of the major sub-basins of the Amazon over 2003-2010 to characterize the redistribution of precipitation on land water. The time-variations of land water storage derived from GRACE are consistent with those of rainfall and discharges at basin and sub-basin scales even at interannual time-scales (correlation generally greater than 0.7). The study of the relationship between these two quantities reveals large differences in terms of rainfall amount, water storage, time delays, resulting of the water transport among the sub-basins of the Amazon. The analysis of GRACE data has enabled identification of the signature of the recent extreme climatic events (droughts of 2005 and 2010, flood of 2009) on the land water storage, in terms of spatial patterns and intensity. These results are in good agreement with what was observed on independent datasets (water levels and discharges, vegetation activity, forest fires, and drought index), highlighting the interest of gravimetry from space missions for the characterization of the interannual variability of the TWS. GRACE data offer the unique opportunity to monitor the hydrological cycle in ungauged basins where reliable observations of rainfall and discharges are missing.

This paper documents the spatio-temporal evolution of wet- and dry-day frequency (WDF and DDF) in the western Amazon, its relationships with oceanic and atmospheric variability and possible impact on vegetation. WDF and DDF changed significantly during the 1980-2009 period (p<0.05). An increase in WDF is observed after 1995 over the northern part of the western Amazon (Maranon basin). The average annual value of WDF changed from 22 days per year before 1995 to 34 days after that date (+55% after 1995). In contrast, DDF increased significantly over the central and southern part of this region (Ucayali basin) after 1986. Average annual DDF was 16.2 days before 1986 and 23.8 days afterwards (+47% after 1986). Interannual variability in WDF appears to be modulated by changes in Pacific SST and the Walker cell during the November-to-March season. This mechanism enhances convective activity over the northern part of the western Amazon. The increase in DDF is related to warming of the North Tropical Atlantic SST, which produces changes in the Hadley cell and subsidence over the central and the southern western Amazon. More intense seasonal hydrological extremes in the western Amazon therefore appear to be related to changes in WDF and DDF that occurred in 1995 and 1986, respectively. During the 2001-2009 period, an index of vegetation condition (NDVI) appears negatively correlated with DDF (r=-0.95; p<0.0001). This suggests that vegetation in the western Amazon is mainly water-limited, rather than light-limited, and indicates that the vegetation is highly sensitive to concentration of rainfall. This article is protected by copyright. All rights reserved.

This Report was prepared for and submitted to the Graduate School of the Ohio State University as a dissertation in partial fulfillment of the requirements for the PhD degree.

The redistribution of atmospheric, oceanic and hydrological masses on the Earth's surface varies in time and this in turn loads and deforms the surface of the solid Earth. Analyzing such environmental loading signal and modeling its induced elastic displacements are of great importance for explaining geophysical phenomena. Based on the well-established loading theory, this thesis makes use of two different space-borne measurements, i.e. GPS and GRACE, along with other environmental loading data to investigate three different aspects of environmental loading and its induced elastic deformations: Firstly, an increasing concern is observed recently over time variable seasonal signals in geodesy. Several model based approaches were applied to extract amplitude and phase modulated annual and semiannual signals. In view of this phenomenon, this thesis introduces an alternative approach, namely, singular spectrum analysis (SSA). With respect to these model-dependent approaches, the advantage of SSA lies in data-driven and model-independence. Several aspects regarding the application of SSA, e.g. optimal choice of window size, are investigated before showing its abilities. Through applying SSA to the lake level time series of Lake Urmia (Iran) and the basin averaged equivalent water height time series of the Congo basin, the capabilities of SSA in separating time varying seasonal signals are demonstrated. In addition, we find that SSA is also able to extract the non-linear trend as well as long-term oscillations from geodetic time series. Secondly, we look into the comparison between GPS and GRACE with an emphasis on GRACE data filtering. Three types of deterministic filters and two types of stochastic filters are studied and compared over GPS sites from two regions, i.e. the Europe area and the Amazon area. The comparisons indicate that no single filtering scheme could provide consistently better performance over other considered filters. However, we find that the stochastic filters generally show better performance than the deterministic filters. The DDK 1 filter outperforms other filters in the Europe area and the regularization filter of parameter lambda=4, which follows the concept of the DDK filters, shows optimal performance in the Amazon area. The combination of the isotropic Gaussian filter of a low smoothing radius, e.g. around 300 km with the destriping filter is proved to be optimal filter choice if only the deterministic filters are considered. Thirdly, based on an overview of displacements modeling at various spatial scales, we evaluate three methods, i.e. two types of half-space approaches and the classic Green function approach, by using a high spatial resolution local load data along the lower Mississippi river when a severe flood happened in 2011. The equivalence between the two half-space approaches, i.e. point load approach and surface load approach, are demonstrated with the local load data. However, the point load approach is recommended for practical use in terms of computational efficiency. In addition, within such a limited spatial extent, we investigate the differences between the half-space approach and the Green function approach. It is shown that the half-space approach predicts larger displacements than the Green function approach and agrees better with the observed deformations at 11 considered GPS sites. Meanwhile, strong global environmental loading effects are found via two global hydrological models, i.e. GLDAS and MERRA. Thus, a reduction of these far-field loading effects beforehand is suggested before probing the local crustal structure using the half-space approach. Last but not least, based on the local load data, the effects of site-dependent Green functions are studied with two types of site-dependent Green functions, which were generated by modifying the local crustal structure of the REF Earth model using the CRUST 1.0 and CRUST 2.0 models. A relative RMS of differences of more than 5% in vertical component and 25% in horizontal components are found with respect to the PREM Earth model based Green functions. It indicates that the Green functions could contribute more uncertainties in loading induced displacements modeling than reported in the literature. Die Massenumverteilungen zwischen Atmosphare, Ozeanen und Hydrologie der Erdoberflache variieren stetig und fuhren im Gegenzug zu Auflasten und Deformationen der festen Erde. Die Untersuchung dieser Auflastsignale und die Modellierung der induzierten elastischen Verformungen sind von enormer Bedeutung fur die Erklarungen geophysikalischer Phanomene. Basierend auf gangigen Theorien werden in dieser Dissertation die beiden Satellitenverfahren GPS und GRACE zusammen mit anderen Auflastdaten genutzt, um drei Aspekte von Auflasten und die von diesen verursachte elastischen Deformationen naher zu untersuchen: Zunachst lasst sich in der Geodasie eine wachsende Tendenz erkennen, auch zeitlich variable, saisonale Signale besser zu untersuchen. Zahlreiche, auf Modellen basierende Verfahren werden angewendet, um Amplituden und Phasen aus jahrlichen und halbjahrlichen Signalen zu erfassen. Fur diese Aufgabe wird in dieser Arbeit die "singular spectrum analysis" (SSA) als alternative Methode dargestellt. Im Gegensatz zu den ublichen modellbasierten Verfahren arbeitet die SSA modellunabhangig nur auf Grundlage der Daten. Verschiedene Freiheitsgrade in der SSA, wie zum Beispiel die Wahl der optimalen Fenstergrose, werden vor der Verwendung untersucht. Die Fahigkeit der SSA, saisonale Signale zu trennen, wird sowohl an der Bestimmung des Wasserstandes fur den Urmia-See (Iran) als auch anhand der Zeitreihe der uber dem Kongo-Becken gemittelten Massenanderungen demonstriert. Zusatzlich konnen mit SSA auch nichtlineare Trends und langfristige Oszillationen aus geodatischen Zeitreihen extrahiert werden. Zweitens werden die Daten von GPS und GRACE unter besonderer Berucksichtigung der Filterung von GRACE-Daten verglichen. Drei Arten deterministischer Filter sowie zwei Arten stochastischer Filter werden untersucht und fur die GPS-Stationen von zwei ausgewahlten Regionen in Mitteleuropa und dem Amazonasgebiet gegenubergestellt. Der Vergleich bestatigt, dass keiner der Filter grundsatzlich den Anderen uberlegen ist. Jedoch kann gezeigt werden, dass die stochastischen Filter im Allgemeinen besser abschneiden als die deterministischen Ansatze. Der DDK 1 Filter fuhrt in Europa zu den besten Ergebnissen, wahrend ein "Regularisierungsfilter", der dem Konzept der DDK Filter nachempfunden ist, mit einem Regularisierungsparameter lambda = 4 die besten Ergebnisse in fur das Amazonasbecken liefert. Die Kombination aus einem isotropen Gausfilter mit kleinem Glattungsradius, z.B. 300 km, und einem "De-striping"-Filter konnte als optimaler Filter bestatigt werden, solange nur deterministische Filter verwendet werden Drittens werden nach einer Zusammenfassung der Deformationsmodelle auf verschiedenen raumlichen Skalen drei Methoden -- zwei Arten von "half-space"-Ansatzen und die klassische Methode der Greenfunktionen -- auf die raumlich hochauflosenden Daten der Uberflutungen im Mississippi-Becken (2011) angewendet. Die Gleichwertigkeit der beiden "half-space"-Ansatze, einerseits der "point load approach" und andererseits der "surface load approach" werden fur lokale Auflastdaten bestatigt. Aus Grunden der numerischen Effizienz ist jedoch der "point load approach" zu bevorzugen. Auserdem werden innerhalb des raumlich begrenzten Gebietes auch die Unterschiede zwischen den "half-space"-Ansatzen und die Methode der Greenfunktionen gegenubergestellt. Es wird gezeigt, dass die "half-space"-Ansatze grosere Deformationen vorhersagen als die Greenfunktionen, wobei die Ergebnisse besser zu den Beobachtungen der 11 GPS-Stationen passen. Inzwischen konnen auch grosere globale Auflasteffekte in den globalen hydrologischen Modellen, wie z.B. GLDAS und MERRA, gefunden werden. Daher wird eine vorherige Reduktion der Fernwirkung von Auflasteffekten empfohlen, ehe man eine lokale Krustenstruktur mit den "half-space"-Ansatzen erforscht. Nicht zuletzt werden auch die Effekte der ortsabhangigen Greenfunktionen fur die lokalen Auflastdaten studiert, wobei die beiden ortsabhangen Varianten der Greenfunktionen aus der lokalen Krustenstruktrur des REF Erdmodells mit den Modellen CRUST 1.0 bzw. CRUST 2.0 erzeugt werden. Im Vergleich zu den Greenfunktionen aus dem PERM Erdmodell wird ein relativer RMS der Differenzen von mehr als 5% in der Vertikalkomponente und 25% in der Horizonalkomponente beobachtet. Daraus kann man ablesen, dass die Greenfunktionen deutlich mehr Unsicherheiten zu den auflastinduzierten Deformationen beitragen, als dies bisher in der Literatur wahrgenommen wird.

The monitoring of hydrological extremes requires water level measurement. Owing to the decreasing number of continuous operating hydrological stations globally, remote sensing indices have been advocated for water level reconstruction recently. Nevertheless, the feasibility of gravimetrically derived terrestrial water storage (TWS) and its corresponding index for water level reconstruction have not been investigated. This paper aims to construct a correlative relationship between observed water level and basin-averaged Gravity Recovery and Climate Experiment (GRACE) TWS and its Drought Severity Index (GRACE-DSI), for the Yangtze river basin on a monthly temporal scale. The results are subsequently compared against traditional remote sensing, Palmer’s Drought Severity Index (PDSI), and El Nino Southern Oscillation (ENSO) indices. Comparison of the water level reconstructed from GRACE TWS and its index, and that of remote sensing against observed water level reveals a Pearson Correlation Coefficient (PCC) above 0.90 and below 0.84, with a Root-Mean-Squares Error (RMSE) of 0.88–1.46 m, and 1.41–1.88 m and a Nash-Sutcliffe model efficiency coefficient (NSE) above 0.81 and below 0.70, respectively. The ENSO-reconstructed water levels are comparable to those based on remote sensing, whereas the PDSI-reconstructed water level shows a similar performance to that of GRACE TWS. The water level predicted at the location of another station also exhibits a similar performance. It is anticipated that the basin-averaged, remotely-sensed hydrological variables and their standardized forms (e.g., GRACE TWS and GRACE-DSI) are viable alternatives for reconstructing water levels for large river basins affected by the hydrological extremes under ENSO influence.

The mapping and forecasting of droughts and floods is an important potential field of application of global soil moisture and water storage products from satellites and models. Especially when extremes in near-surface soil moisture propagate into extremes in total water storage, agricultural production and water supply can be severely impacted. This study relates soil moisture from the WaterGAP Global Hydrology Model (WGHM) and the satellite sensors Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) and Advanced Scatterometer (ASCAT) to total water storage variations from the satellite gravity mission GRACE. A particular focus is on destructive hydrological extreme events, as listed in the International Disaster Database EM-DAT. Data sets are analyzed via correlation, time shift, and principal component analyses. The study area is the La Plata Basin in South America. The results indicate that most of the soil moisture anomalies are linked to periods of El Nino and La Nina and associated natural disasters. For the La Plata drought of 2008/2009 and the El Nino flooding of 2009/2010, soil moisture serves as an indicator for the later deficit or surplus in total water storage. These hydrological anomalies were strongest in the southern, central, and eastern parts of the basin, but more than one hundred thousand people were also affected in the northwestern part.

The gravity field of the Earth, caused by the distribution of masses inside and on the surface of the Earth, changes in time due to the redistribution of mass. Such mass fluxes can be due both to natural processes (such as the seasonal water cycle, ocean dynamics, or atmospheric variations), as well as due to human actions, such as the systematic withdrawal of groundwater for human consumption. The ability to measure such changes globally is of great significance for understanding the environmental dimension of sustainability.

The availability of data is a major challenge for hydrological modelling in large parts of the world. Remote sensing data can be exploited to improve models of ungauged or poorly gauged catchments. In this study we combine three datasets for calibration of a rainfall-runoff model of the poorly gauged Okavango catchment in Southern Africa: (i) surface soil moisture (SSM) estimates derived from radar measurements onboard the Envisat satellite; (ii) radar altimetry measurements by Envisat providing river stages in the tributaries of the Okavango catchment, down to a minimum river width of about one hundred meters; and (iii) temporal changes of the Earth’s gravity field recorded by the Gravity Recovery and Climate Experiment (GRACE) caused by total water storage changes in the catchment. The SSM data are shown to be helpful in identifying periods with overrespectively underestimation of the precipitation input. The accuracy of the radar altimetry data is validated on gauged subbasins of the catchment and altimetry data of an ungauged subbasin is used for model calibration. The radar altimetry data are important to condition model parameters related to channel morphology such as Manning’s roughness. GRACE data are used to validate the model and to condition model parameters related to various storage compartments in the hydrological model (e.g. soil, groundwater, bank storage etc.). As precipitation input the FEWS-Net RFE, TRMM 3B42 and ECMWF ERA-Interim datasets are considered and compared. Correspondence to: C. Milzow (christian.milzow@alumni.ethz.ch)

The US/German twin satellite mission GRACE (Gravity Recovery and Climate Experiment) [1] celebrated it's 10th anniversary March 17, 2012. The prime objective of GRACE is to provide detailed measurements of the static and, for the first time, also of the time-variable gravity field of the Earth. Today, GRACE delivers the most accurate map to date of the Earth's long wavelength gravity field. In view of global warming it can deliver substantial information about current and future trends concerning the global hydrology cycle, ocean currents and sea level rise and help to predict their impact on human live.

The Gravity Recovery and Climate Experiment (GRACE) mission is able to observe the global large-scale mass and water cycle for the first time with unprecedented spatial and temporal resolution. However, no other time-varying gravity fields validate GRACE. Furthermore, the C20 of GRACE is poor, and no GRACE data are available before 2002 and there will likely be a gap between the GRACE and GRACE-FOLLOW-ON mission. To compensate for GRACE’s shortcomings, in this paper, we provide an alternative way to invert Earth’s time-varying gravity field, using a priori degree variance as a constraint on amplitudes of Stoke’s coefficients up to degree and order 60, by combining continuous GPS coordinate time series and satellite altimetry (SA) mean sea level anomaly data from January 2003 to December 2012. Analysis results show that our estimated zonal low-degree gravity coefficients agree well with those of GRACE, and large-scale mass distributions are also investigated and assessed. It was clear that our method effectively detected global large-scale mass changes, which is consistent with GRACE observations and the GLDAS model, revealing the minimums of annual water cycle in the Amazon in September and October. The global mean mass uncertainty of our solution is about two times larger than that of GRACE after applying a Gaussian spatial filter with a half wavelength at 500 km. The sensitivity analysis further shows that ground GPS observations dominate the lower-degree coefficients but fail to contribute to the higher-degree coefficients, while SA plays a complementary role at higher-degree coefficients. Consequently, a comparison in both the spherical harmonic and geographic domain confirms our global inversion for the time-varying gravity field from GPS and Satellite Altimetry.

The Gravity Recovery and Climate Experiment (GRACE) is a satellite gravimetry mission, which provides the mean and time-varying global gravity fields. In this thesis, regional terrestrial water storage and sea level variations from GRACE are investigated. Groundwater storage variations (GWS) in North China are estimated from GRACE, and compared with in situ water table observations, groundwater model, and groundwater bulletins. The rate of groundwater depletion in North China based on GRACE and land surface models is 7.1±1.0 km3/yr from 2003 to 2010. In addition, GRACE detects obvious seasonal variations of seawater mass in the northern shallow water of South China Sea (SCS). On interannual timescales, sea level fluctuations in the SCS are dominated by the thermosteric effect and driven by El Nino-Southern Oscillation (ENSO) events. For the Red Sea, an annual amplitude of ~18 cm mass-induced SLV is detected from GRACE and steric-corrected altimetry from 2003 to 2011, which is well explained by the water exchange between the Red Sea and the Gulf of Aden, which is driven by the seasonal reversal of monsoon.

Summary Shortwave vegetation index (VI) and leaf area index (LAI) remote sensing products yield inconsistent depictions of biophysical response to drought and pluvial events that have occurred in Brazil over the past decade. Conflicting reports of severity of drought impacts on vegetation health and functioning have been attributed to cloud and aerosol contamination of shortwave reflectance composites, particularly over the rainforested regions of the Amazon basin which are subject to prolonged periods of cloud cover and episodes of intense biomass burning. This study compares timeseries of satellite-derived maps of LAI from the Moderate Resolution Imaging Spectroradiometer (MODIS) and precipitation from the Tropical Rainfall Mapping Mission (TRMM) with a diagnostic Evaporative Stress Index (ESI) retrieved using thermal infrared remote sensing over South America for the period 2003–2013. This period includes several severe droughts and floods that occurred both over the Amazon and over unforested savanna and agricultural areas in Brazil. Cross-correlations between absolute values and standardized anomalies in monthly LAI and precipitation composites as well as the actual-to-reference evapotranspiration (ET) ratio used in the ESI were computed for representative forested and agricultural regions. The correlation analyses reveal strong apparent anticorrelation between MODIS LAI and TRMM precipitation anomalies over the Amazon, but better coupling over regions vegetated with shorter grass and crop canopies. The ESI was more consistently correlated with precipitation patterns over both landcover types. Temporal comparisons between ESI and TRMM anomalies suggest longer moisture buffering timescales in the deeper rooted rainforest systems. Diagnostic thermal-based retrievals of ET and ET anomalies, such as used in the ESI, provide independent information on the impacts of extreme hydrologic events on vegetation health in comparison with VI and precipitation-based drought indicators, and used in concert may provide a more reliable evaluation of natural and managed ecosystem response to variable climate regimes.