[1] We have produced a 0.5° × 0.5° monthly global soil moisture data set for the period from 1948 to the present. The land model is a one-layer “bucket” water balance model, while the driving input fields are Climate Prediction Center monthly global precipitation over land, which uses over 17,000 gauges worldwide, and monthly global temperature from global Reanalysis. The output consists of global monthly soil moisture, evaporation, and runoff, starting from January 1948. A distinguishing feature of this data set is that all fields are updated monthly, which greatly enhances utility for near-real-time purposes. Data validation shows that the land model does well; both the simulated annual cycle and interannual variability of soil moisture are reasonably good against the limited observations in different regions. A data analysis reveals that, on average, the land surface water balance components have a stronger annual cycle in the Southern Hemisphere than those in the Northern Hemisphere. From the point of view of soil moisture, climates can be characterized into two types, monsoonal and midlatitude climates, with the monsoonal ones covering most of the low-latitude land areas and showing a more prominent annual variation. A global soil moisture empirical orthogonal function analysis and time series of hemisphere means reveal some interesting patterns (like El Nino-Southern Oscillation) and long-term trends in both regional and global scales.

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 examine the relative contributions of root zone soil moisture (SM) and groundwater (GW) storage to trends and interannual variability in total water storage (TWS) from essentially the entire Gravity Recovery and Climate Experiment (GRACE) record (2003–2016). Our study region is the Eastern United States where GW generally is shallow, and we use observations and simulations from a suite of land surface models (LSMs) where observations are not available. We first consider Illinois, which has a long‐term network of SM sensors and observation wells. We assessed the uncertainties in GRACE TWS in comparison with the observation‐based TWS using observed SM, well‐derived GW, and model‐reconstructed snow water equivalent. We evaluated LSM SM compared with observations over Illinois; generally good agreement encouraged us to use the LSM‐ensemble average SM, well‐derived GW, and Snow Data Assimilation System snow water equivalent over the entire Eastern United States to examine the relative contribution of storage components to GRACE TWS over this larger domain. Taken together, the three components explained 2%–85% of interannual variability in GRACE TWS across the 2‐digit Hydrologic Unit Code regions. Root zone SM was the dominant contributor to the interannual variability (but not trends) of GRACE TWS over much of the Eastern United States. We also compared three independent approaches to estimating GW: well data, baseflow observations, and GRACE. Despite much smaller magnitudes of variation, baseflow‐derived GW correlated more highly with well‐derived GW over the eastern‐most and Upper Mississippi regions, while GRACE‐derived GW matched better with well‐derived GW over the remaining regions.

Water is our most precious and arguably most undervalued natural resource. It is essential for life on our planet, for food production and economic development. Moreover, water plays a fundamental role in shaping weather and climate. However, with the growing global population, the planet’s water resources are constantly under threat from overuse and pollution. In addition, the effects of a changing climate are thought to be leading to an increased frequency of extreme weather causing floods, landslides and drought. The need to understand and monitor our environment and its resources, including advancing our knowledge of the hydrological cycle, has never been more important and apparent. The best approach to do so on a global scale is from space. This paper provides an overview of the major components of the hydrological cycle, the status of their observations from space and related data products and models for hydrological variable retrievals. It also lists the current and planned satellite missions contributing to advancing our understanding of the hydrological cycle on a global scale. Further details of the hydrological cycle are substantiated in several of the other papers in this Special Issue.

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 volume provides in-depth coverage of the latest in remote sensing of hydrological extremes: both floods and droughts. The book is divided into two distinct sections floods and droughts and offers a variety of techniques for monitoring each. With rapid advances in computer modelling and observing systems, floods and droughts are studied with greater precision today than ever before. Land surface models, especially over the entire Continental United States, can map the hydrological cycle at kilometre and sub-kilometre scales. In the case of smaller areas there is even higher spatial resolution and the only limiting factor is the resolution of input data. In-situ sensors are automated and the data is directly relayed to the world wide web for many hydrological variables such as precipitation, soil moisture, surface temperature and heat fluxes. In addition, satellite remote sensing has advanced to providing twice a day repeat observations at kilometre to ten-kilometre spatial scales. We are at a critical juncture in the study of hydrological extremes, and the GPM and SMAP missions as well as the MODIS and GRACE sensors give us more tools and data than were ever available before. A global variety of chapter authors provides wide-ranging perspectives and case studies that will make this book an indispensable resource for researchers, engineers, and even emergency management and insurance professionals who study and/or manage hydrological extremes.

This study presents the first comparison of seasonal groundwater storage (GWS) variations derived from GRACE satellite data with groundwater‐level measurements in the High Plains Aquifer, USA (450,000 km2). Correlation between seasonal GRACE terrestrial water storage (TWS) and the sum of GWS estimated from field measurements (2,700 wells) and soil moisture (SM) simulated by a land surface model is high (R = 0.82). Correlation between GRACE‐derived and measured GWS is also significant (R = 0.58). Seasonal GRACE‐derived TWS and GWS changes were detectable (≥ uncertainty) in 7 and 5 out of 9 monitored periods respectively whereas maximum changes (between winter/spring and summer/fall) in TWS and GWS were detectable in all 5 monitored periods. These results show the potential for GRACE to monitor GWS changes in semiarid regions where irrigation pumpage causes large seasonal GWS variations.

This paper presents a continental‐scale phenological analysis of African savannas and woodlands. We apply an array of synergistic vegetation and hydrological data records from satellite remote sensing and model simulations to explore the influence of rainy season timing and duration on regional land surface phenology and ecosystem structure. We find that (i) the rainy season onset precedes and is an effective predictor of the growing season onset in African grasslands. (ii) African woodlands generally have early green‐up before rainy season onset and have a variable delayed senescence period after the rainy season, with this delay correlated nonlinearly with tree fraction. These woodland responses suggest their complex water use mechanisms (either from potential groundwater use by relatively deep roots or stem‐water reserve) to maintain dry season activity. (iii) We empirically find that the rainy season length has strong nonlinear impacts on tree fractional cover in the annual rainfall range from 600 to 1800 mm/yr, which may lend some support to the previous modeling study that given the same amount of total rainfall to the tree fraction may first increase with the lengthening of rainy season until reaching an “optimal rainy season length,” after which tree fraction decreases with the further lengthening of rainy season. This nonlinear response is resulted from compound mechanisms of hydrological cycle, fire, and other factors. We conclude that African savannas and deciduous woodlands have distinctive responses in their phenology and ecosystem functioning to rainy season. Further research is needed to address interaction between groundwater and tropical woodland as well as to explicitly consider the ecological significance of rainy season length under climate change.

The essence of this paper is to undertake an investigation of the recent Canadian Prairie drought by employing total water storage anomalies obtained from gravity recovery and climate experiment (GRACE) remote sensing satellite mission. In order to successfully retrieve average terrestrial water storages from gravity measurements, a necessary procedure is to undertake the transformation of the GRACE geopotential spherical harmonic coefficients into spatially varying time series of geopotential heights that were subsequently converted into water equivalent amounts. These obtained GRACE-based total water storages were thereafter validated using storages estimated from the atmospheric-based water balance P - E computation in conjunction with the measured streamflow records for the Saskatchewan River Basin at its Grand Rapids outlet in Canada. Interestingly, the results from this study corroborate the potential of GRACE-based technique as a veritable tool for the characterization of the 2002/2003 Canadian Prairie droughts. Especially, this approach would prove resourceful for other regions globally where soil moisture availability is sparing or worst still, inexistent thereby making such studies impossible.

The Indian Ocean Tsunami of December 26, 2004 devastated coastal ecosystems across South Asia. Along the coastal regions of South India, increased groundwater levels (GWL), largely caused by saltwater intrusion, infiltration from inundated land, and disturbance of freshwater lenses, were reported. Many agencies allocated funding for restoration and rehabilitation projects. However, to streamline funding allocation efforts, district-level groundwater inundation/recession data would have been a useful tool for planners. Thus, to ensure better preparedness for future disaster relief operations, it is crucial to quantify pre- and post-tsunami groundwater levels across coastal districts in India. Since regional scale GWL field observations are not often available, this study instead used space gravimetry data from NASA’s Gravity Recovery and Climate Experiment (GRACE), along with soil moisture data from the Global Land Data Assimilation Systems (GLDAS), to quantify GWL fluctuations caused by the tsunami. A time-series analysis of equivalent groundwater thickness was developed for February 2004–December 2005 and the results indicated a net increase of 274 % in GWLs along coastal regions in Tamil Nadu following the tsunami. The net recharge volume of groundwater due to the tsunami was 16.8 km3, just 15 % lower than the total annual groundwater recharge (19.8 km3) for the state of Tamil Nadu. Additionally, GWLs returned to average within 3 months following the tsunami. The analysis demonstrated the utility of remotely sensed data in predicting and assessing the impacts of natural disasters.

The Huang-Huai-Hai (3H) Plain is the major crop-producing region in China. Due to the long-term overexploitation of groundwater for irrigation, the groundwater funnel is constantly expanding and the scarcity of water resources is prominent in this region. In this study, Gravity Recovery and Climate Experiment (GRACE) and hydrological models were used to estimate the spatial-temporal changes of groundwater storage (GWS) and the driving factors of GWS variations were discussed in the 3H Plain. The results showed that GRACE-based GWS was depleted at a rate of −1.14 ± 0.89 cm/y in the 3H Plain during 2003 to 2015. The maximum negative anomaly occurred in spring due to agricultural irrigation activities. Spatially, the loss of GWS in the Haihe River Basin is more serious than that in the Huaihe River Basin, presenting a decreasing trend from south to north. Conversely, the blue water footprint (WFblue) of wheat exhibited an increasing trend from south to north. During the drought years of 2006, 2013, and 2014, more groundwater was extracted to offset the surface water shortage, leading to an accelerated decline in GWS. This study demonstrated that GWS depletion in the 3H Plain is well explained by reduced precipitation and groundwater abstraction due to anthropogenic irrigation activities.

The Gravity Recovery and Climate Experiment (GRACE) satellite mission, which was in operation from March 2002 to June 2017, was the first remote sensing mission to provide temporal variations of Terrestrial Water Storage (TWS), which is the sum of the water masses that were contained in the soil column (i.e., snow, surface water, soil moisture, and groundwater), at a spatial resolution of a few hundred kilometers. As in situ level measurements are generally not sufficiently available for monitoring groundwater changes at the regional-scale, this unique dataset, combined with external information, is widely used to quantify the interannual variations of groundwater storage in the world’s major aquifers. GRACE-based groundwater changes revealed significant aquifer depletion over large regions, such as the Middle East, the northwest India aquifer, the North China Plain aquifer, the Murray-Darling Basin in Australia, the High Plains, and the California Central Valley aquifers in the United States of America (USA), but were also used to estimate groundwater-related parameters such as the specific yield, which relates groundwater level to storage, or to define the indices of groundwater depletion and stress. In this review, the approaches used for estimating groundwater storage variations are presented along with the main applications of GRACE data for groundwater monitoring. Issues that were related to the use of GRACE-based TWS are also addressed.

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 This work presents an iterative, water balance based approach to estimate actual evapotranspiration (ET) with integrated surface/subsurface flow models. Traditionally, groundwater level fluctuation methods have been widely accepted and used for estimating ET and net groundwater recharge; however, in watersheds where interactions between surface and subsurface flow regimes are highly dynamic, the traditional method may be overly simplistic. Here, an innovative methodology is derived and demonstrated for using the water balance equation in conjunction with a fully-integrated surface and subsurface hydrologic model (HydroGeoSphere) in order to estimate ET at watershed and sub-watershed scales. The method invokes a simple and robust iterative numerical solution. For the proof of concept demonstrations, the method is used to estimate ET for a simple synthetic watershed and then for a real, highly-characterized 7000 km2 watershed in Southern Ontario, Canada (Grand River Watershed). The results for the Grand River Watershed show that with three to five iterations, the solution converges to a result where there is less than 1% relative error in stream flow calibration at 16 stream gauging stations. The spatially-averaged ET estimated using the iterative method shows a high level of agreement (R2 = 0.99) with that from a benchmark case simulated with an ET model embedded directly in HydroGeoSphere. The new approach presented here is applicable to any watershed that is suited for integrated surface water/groundwater flow modelling and where spatially-averaged ET estimates are useful for calibrating modelled stream discharge.

Summary Retrieval of the terrestrial moisture storage dataset from the Gravity Recovery and Climate Experiment (GRACE) satellite remote sensing system is possible when the catchment of interest is of large spatial scale. These dataset are of paramount importance for the estimation of the total storage deficit index (TSDI), which enables the characterization of a particular drought event from the perspective of the terrestrial moisture storage over that catchment. Incidentally, the GRACE gravity signal over the 13,000 km 2 Upper Assiniboine River Basin on the drought-prone Canadian Prairie is so poor therefore making the computation of the total storage deficit index for this basin infeasible. Consequently, the estimation of the terrestrial moisture storage from other reliable sources becomes imperative in order to enable the computation of the TSDI over this basin. This study explores the utilization of the Variable Infiltration Capacity (VIC) model, a physically based, spatially distributed hydrologic model to simulate the total moisture storage over the Upper Assiniboine River Basin which was then employed in the estimation of the TSDI over this basin for subsequent characterization of the recent Prairie-wide drought. Interestingly, the temporal patterns in the computed TSDI from the VIC model reveal a strong resemblance with the same drought characterization undertaken over the larger adjacent Saskatchewan River Basin, which was accomplished utilizing terrestrial moisture storage from the GRACE-based approach. Additionally, these independent techniques employed in the characterization of the last Prairie drought over the two adjacently situated basins resulted in similar drought severity classification from the standpoint of the total moisture storage deficits over these basins. This study has therefore shown that in the computation of the total storage deficit index over small-scale catchments during anomalous climatic conditions that propagate extreme dryness through the terrestrial hydrologic systems, simulations of the total water storage from a structurally sound model such as the VIC model could be resourceful for the computation of the monthly total storage deficit index if no constraint is placed on the availability of accurate meteorological forcing.

Summary Groundwater trade is widely advocated for reallocating scarce groundwater resources between competing users, and managing over-allocated and declining aquifers. However, groundwater markets are still in their infancy, and the potential benefits and opportunities need investigation, particularly where there is a need to reduce the extraction from declining aquifers. This article evaluates economic impacts of reducing groundwater extraction for irrigation use in peri-urban Perth, Australia, where irrigation, a lake-based ecosystem, and public water supply are highly dependent on a declining groundwater resource. We present an assessment of market-based water trading approaches to reduce groundwater extraction with an economic model representing diversity in returns to groundwater use across a population of irrigators. The results indicate that potential economic costs of a proportional reduction in available groundwater for irrigation are 18–21% less if groundwater trade is possible. We also evaluate a water buyback from irrigation to provide public water supply as an alternative to new infrastructure. We find that buying back up to around 50% of current irrigation allocations could create new public water supply only at the cost of $0.32–0.39 million per GL, which is less than one fifth of the costs of new desalinisation or recycled water supply options ($2–3 million per GL). We conclude that, with rapid development of computer and internet based trading platforms that allows fast, efficient and low cost multiple party trading, it is increasingly feasible to realise the economic potentials of market-based trade approaches for managing overexploited aquifers.

Streamflow of the Colorado River Basin is the most overallocated in the world. Recent assessment indicates that demand for this renewable resource will soon outstrip supply, suggesting that limited groundwater reserves will play an increasingly important role in meeting future water needs. Here we analyze 9 years (December 2004 to November 2013) of observations from the NASA Gravity Recovery and Climate Experiment mission and find that during this period of sustained drought, groundwater accounted for 50.1 km3 of the total 64.8 km3 of freshwater loss. The rapid rate of depletion of groundwater storage (−5.6 ± 0.4 km3 yr−1) far exceeded the rate of depletion of Lake Powell and Lake Mead. Results indicate that groundwater may comprise a far greater fraction of Basin water use than previously recognized, in particular during drought, and that its disappearance may threaten the long‐term ability to meet future allocations to the seven Basin states.

Since its launch in March 2002, the Gravity Recovery and Climate Experiment (GRACE) has provided a global mapping of the time-variations of the Earth’s gravity field. Tiny variations of gravity from monthly to decadal time scales are mainly due to redistributions of water mass inside the surface fluid envelops of our planet (i.e., atmosphere, ocean and water storage on continents). In this article, we present a review of the major contributions of GRACE satellite gravimetry in global and regional hydrology. To date, many studies have focused on the ability of GRACE to detect, for the very first time, the time-variations of continental water storage (including surface waters, soil moisture, groundwater, as well as snow pack at high latitudes) at the unprecedented resolution of ~400–500 km. As no global complete network of surface hydrological observations exists, the advances of satellite gravimetry to monitor terrestrial water storage are significant and unique for determining changes in total water storage and water balance closure at regional and continental scales.

Satellite measurements of time-variable gravity are a new data type, capable of addressing a wide variety of geophysical problems. This subject, in its present form, began with the 2002 launch of the Gravity Recovery and Climate Experiment (GRACE). GRACE has been providing regular monthly estimates of the Earth’s gravity field down to scales of several hundred kilometers. Any process that involves enough redistribution of mass at those temporal and spatial scales is a possible target for GRACE. There are applications for hydrology, oceanography, the cryosphere, and the solid Earth. This chapter summarizes the observational and theoretical framework used to interpret time-variable satellite gravity measurements, and reviews some of the evolving geophysical applications of this technique.

SUMMARY Successfully launched in 2002 mid-March, the goal of the Gravity Recovery And Climate Experiment (GRACE) satellite mission is to measure the spatio-temporal variations of the gravity field of the Earth to high accuracy (∼1 cm in terms of geoid height) and a spatial resolution of ∼200–300 km, for a nominal period of 5 yr. The unprecedented precision of the GRACE mission will enable us to detect tiny time variations of the gravity field related to global redistributions of water and air mass inside fluid envelops at the surface of the Earth. In this paper, we present a new approach based on linear inverse methods to separate the different contributions of the main surface fluid reservoirs (oceans, atmosphere, total continental water storage including snow, soil wetness, ground water and ice caps) from monthly synthetic GRACE geoids. The synthetic geoids were computed from outputs of global models of different climatic fields. Because of the non-uniqueness of the classical inverse problems in gravimetry, independent information was added before inverting the synthetic geoids. Geoid solutions associated with each fluid contribution were then converted into water-equivalent thickness maps. Validation of the continental water storage solutions was performed by comparing total soil water estimates [soil moisture (SM) plus groundwater] with predictions of a global hydrological model in 71 different drainage basins of the world. Analysis of the a posteriori errors of the solutions suggests that the inversion method developed in this study allows recovering monthly water mass changes with a cm precision.