article The measurement of total basin water discharge is important for understanding the hydrological and climatologic issues related to the water and energy cycles. Climatic extreme events are normal climatic occur- rences in Africa. For example, extensive droughts are regular features in the last few decades in parts of East Africa, which suffers from a lack of in situ observations as well as a lack of regional hydrological models. In this study, multi-disciplinary different types of space-borne observations and global hydrological models are used to study total water discharge in the Great Rift Valley of East Africa (i.e. Lakes Victoria, Tanganyika, and Malawi)fromJanuary2003toDecember2012.Thedataincludethefollowing:(1)totalwaterstorage(TWS)var- iations from Gravity Recovery and Climate Experiment (GRACE), (2) the lake level variations from Satellite Alimetric data, (3) rainfall from Tropical Rainfall Measurement Mission (TRMM) products, (4) soil moisture from WaterGAP Global Hydrology Model (WGHM), and (5) water fluxes from Global Land Data Assimilation System (GLDAS). Results show that a significant decline in the average lake level is found for all of the three lakes between 2003 and 2006. GRACE TWS variations of thewhole basinareashow thesamepattern of variation as the average lake level variations estimated from Altimetric data. The TWS in the basin area of Lakes Victoria and Malawi is governed by the surface water stored in each lake itself, while for Lake Tanganyika, it is governed byboth surface waterandthe soil moisture contentinthebasinarea.Furthermore,theeffect of rainfall onTWS is also studied. A phase lag of ~2 months is found between TRMM rainfall and GRACE TWS (generally, rainfall pre- cedes the GRACE TWS) for the three lakes. In addition, the regional evapotranspiration ET is estimated from the water balance equation using GRACE land-water solutions, rainfall data from TRMM and runoff values obtained as a fraction of rainfall. It is found that the computed ET represents approximately 90% of the rainfall over the study region.

[1] The purpose of this work is to investigate the feasibility of downscaling Gravity Recovery and Climate Experiment (GRACE) satellite data for predicting groundwater level changes and, thus, enhancing current capability for sustainable water resources management. In many parts of the world, water management decisions are traditionally informed by in situ observation networks which, unfortunately, have seen a decline in coverage in recent years. Since its launch, GRACE has provided terrestrial water storage change (ΔTWS) data at global and regional scales. The application of GRACE data for local-scale groundwater resources management has been limited because of uncertainties inherent in GRACE data and difficulties in disaggregating various TWS components. In this work, artificial neural network (ANN) models are developed to predict groundwater level changes directly by using a gridded GRACE product and other publicly available hydrometeorological data sets. As a feasibility study, ensemble ANN models are used to predict monthly and seasonal water level changes for several wells located in different regions across the US. Results indicate that GRACE data play a modest but significantly role in the performance of ANN ensembles, especially when the cyclic pattern of groundwater hydrograph is disrupted by extreme climate events, such as the recent Midwest droughts. The statistical downscaling approach taken here may be readily integrated into local water resources planning activities.

[1] The proposed Surface Water and Ocean Topography (SWOT) mission would provide measurements of water surface elevation (WSE) for characterization of storage change and discharge. River channel bathymetry is a significant source of uncertainty in estimating discharge from WSE measurements, however. In this paper, we demonstrate an ensemble-based data assimilation (DA) methodology for estimating bathymetric depth and slope from WSE measurements and the LISFLOOD-FP hydrodynamic model. We performed two proof-of-concept experiments using synthetically generated SWOT measurements. The experiments demonstrated that bathymetric depth and slope can be estimated to within 3.0 microradians or 50 cm, respectively, using SWOT WSE measurements, within the context of our DA and modeling framework. We found that channel bathymetry estimation accuracy is relatively insensitive to SWOT measurement error, because uncertainty in LISFLOOD-FP inputs (such as channel roughness and upstream boundary conditions) is likely to be of greater magnitude than measurement error.

[1] In this study we estimate a time series of regional groundwater anomalies by combining terrestrial water storage estimates from the Gravity Recovery and Climate Experiment (GRACE) satellite mission with in situ soil moisture observations from the Oklahoma Mesonet. Using supplementary data from the Department of Energy's Atmospheric Radiation Measurement (DOE ARM) network, we develop an empirical scaling factor with which to relate the soil moisture variability in the top 75 cm sampled by the Mesonet sites to the total variability in the upper 4 m of the unsaturated zone. By subtracting this estimate of the full unsaturated zone soil moisture anomalies, we arrive at a time series of groundwater anomalies, spatially averaged over a region approximately 280,000 km2 in area. Results are compared to observed well level data from a larger surrounding region, and show consistent phase and relative inter-annual variability.

[1] In recent publications, a new basin-scale dataset of monthly variations in terrestrial water storage (BSWB) was derived for the ERA40 time period (1958–2002) using an atmospheric-terrestrial water-balance approach (Seneviratne et al., 2004; Hirschi et al., 2006). Here, we test the feasibility of using ECMWF operational forecast analyses – available for the recent time period in near real time – instead of reanalysis data for the derivation of these estimates. Our results suggest that the moisture flux convergence from the ECMWF operational forecast analysis is generally consistent with ERA40 in the investigated regions, including 35 mid-latitude river basins and domains. For ten domains with recent streamflow measurements, water-balance estimates of monthly terrestrial water storage variations derived using the ECMWF operational forecast data are compared with estimates from the Gravity Recovery and Climate Experiment (GRACE). In general the atmospheric-terrestrial water-balance estimates show more geographical detail than the analyzed standard resolution GRACE products.

[1] While continental water storage plays a key role in the Earth’s water, energy, and biogeochemical cycles, its temporal and spatial variations are poorly known, in particular, for large areas. This study analyzes water storage simulated with the Watergap Global Hydrology Model. The model represents four major storage compartments: surface water, snow, soil, and groundwater. Water storage variations are analyzed for the period 1961–1995 with 0.5 resolution, for the major global climate zones, and for the 30 largest river basins worldwide. Seasonal variations are the dominant storage change signal with maximum values in the marginal tropics and in snow-dominated high-latitude areas.

We use Monte Carlo analysis to show that explicit representation of an aquifer within a land-surface model (LSM) decreases the dependence of model performance on accurate selection of subsurface hydrologic parameters. Within the National Center for Atmospheric Research Community Land Model (CLM) we evaluate three parameterizations of vertical water flow: (1) a shallow soil profile that is characteristic of standard LSMs; (2) an extended soil profile that allows for greater variation in terrestrial water storage; and (3) a lumped, unconfined aquifer model coupled to the shallow soil profile. North American Land Data Assimilation System meteorological forcing data (1997–2005) drive the models as a single column representing Illinois, USA. The three versions of CLM are each run 22,500 times using a random sample of the parameter space for soil texture and key hydrologic parameters. Other parameters remain constant. Observation-based monthly changes in state-averaged terrestrial water storage (dTWS) are used to evaluate the model simulations. After single-criteria parameter exploration, the schemes are equivalently adept at simulating dTWS. However, explicit representation of groundwater considerably decreases the sensitivity of modeled dTWS to errant parameter choices. We show that approximate knowledge of parameter values is not sufficient to guarantee realistic model performance: because interaction among parameters is significant, they must be prescribed as a congruent set.

We investigate the interannual variability over 2003-2008 of different hydrological parameters in the Amazon river basin: (1) vertically-integrated water storage from the GRACE space gravimetry mission, (2) surface water level of the Amazon River and its tributaries from in situ gauge stations, and (3) precipitation. We analyze the spatio-temporal evolution of total water storage from GRACE and in situ river level along the Amazon River and its main tributaries and note significant differences between the various parts of the basin. We also perform an Empirical Orthogonal Decomposition of total water storage, river level and precipitation over the whole basin. We find that the 2003-2008 period, is characterized by two major hydrological events: a temporary drought in late 2005 that affected the western and central parts of the basin and very wet conditions peaking in mid-2006, in the eastern, northern and southern regions of the basin. Derivative of basin-average water storage from GRACE is shown to be highly correlated with the Southern Oscillation Index (a proxy of ENSO -- El Nino-Southern Oscillation), confirming that the spatio-temporal change in hydrology of the Amazon basin is at least partly driven by the ENSO phenomenon, as noticed in previous studies.

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.

We evaluate the impact of Gravity Recovery and Climate Experiment data assimilation (GRACE-DA) on seasonal hydrological forecast initialization over the U.S., focusing on groundwater storage. GRACE-based terrestrial water storage (TWS) estimates are assimilated into a land surface model for the 2003-2016 period. Three-month hindcast (i.e., forecast of past events) simulations are initialized using states from the reference (no data assimilation) and GRACE-DA runs. Differences between the two initial hydrological condition (IHC) sets are evaluated for two forecast techniques at 305 wells where depth-to-water-table measurements are available. Results show that using GRACE-DA-based IHC improves seasonal groundwater forecast performance in terms of both RMSE and correlation. While most regions show improvement, degradation is common in the High Plains, where withdrawals for irrigation practices affect groundwater variability more strongly than the weather variability, which demonstrates the need for simulating such activities. These findings contribute to recent efforts towards an improved U.S. drought monitor and forecast system.

We estimate terrestrial water storage variations using time variable gravity changes observed by the Gravity Recovery and Climate Experiment (GRACE) satellites during the first 2 years of the mission. We examine how treatment of low-degree gravitational changes and geocenter variations affect GRACE based estimates of basin-scale water storage changes, using independently derived low-degree harmonics from Earth rotation (EOP) and satellite laser ranging (SLR) observations. GRACE based water storage changes are compared with estimates from NASA's Global Land Data Assimilation System (GLDAS). Results from the 22 GRACE monthly gravity solutions, covering the period April 2002 to July 2004, show remarkably good agreement with GLDAS in the Mississippi, Amazon, Ganges, Ob, Zambezi, and Victoria basins. Combining GRACE observations with EOP and SLR degree-2 spherical harmonic coefficient changes and SLR observed geocenter variations significantly affects and apparently improves the estimates, especially in the Mississippi, Ob, and Victoria basins.

Understanding water storage changes within the Nile’s main sub-basins and the related impacts of climate variability is an essential step in managing its water resources. The Gravity Recovery And Climate Experiment (GRACE) satellite mission provides a unique opportunity to monitor changes in total water storage (TWS) of large river basins such as the Nile. Use of GRACE-TWS changes for monitoring the Nile is, however, difficult since stronger TWS signals over the Lake Victoria Basin (LVB) and the Red Sea obscure those from smaller sub-basins making their analysis difficult to undertake. To mitigate this problem, this study employed Independent Component Analysis (ICA) to extract statistically independent TWS patterns over the sub-basins from GRACE and the Global Land Data Assimilation System (GLDAS) model. Monthly precipitation from the Tropical Rainfall Measuring Mission (TRMM) over the entire Nile Basin are also analysed by ICA. Such extraction enables an in-depth analysis of water storage changes within each sub-basin and provides a tool for assessing the influence of anthropogenic as well as climate variability caused by large scale ocean–atmosphere interactions such as the El Nino Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). Our results indicate that LVB experienced effects of both anthropogenic and climate variability (i.e., a correlation of 0.56 between TWS changes and IOD at 95% confidence level) during the study period 2002–2011, with a sharp drop in rainfall between November and December 2010, the lowest during the entire study period, and coinciding with the drought that affected the Greater Horn of Africa. Ethiopian Highlands (EH) generally exhibited a declining trend in the annual rainfall over the study period, which worsened during 2007–2010, possibly contributing to the 2011 drought over GHA. A correlation of 0.56 was found between ENSO and TWS changes over EH indicating ENSO’s dominant influence. TWS changes over Bar-el-Ghazal experienced mixed increase–decrease, with ENSO being the dominant climate variability in the region during the study period. A remarkable signal is noticed over the Lake Nasser region indicating the possibility of the region losing water not only through evaporation, but also possibly through over extraction from wells in the Western Plateau (Nubian aquifer).

This study presents the first direct comparison of terrestrial water storage estimates from the Gravity Recovery and Climate Experiment (GRACE) satellite mission to in situ hydrological observations. Monthly anomalies of total water storage derived from GRACE gravity fields are compared with combined soil moisture and groundwater measurements from a network of observing sites in Illinois. This comparison is achieved through the use of a recently developed filtering technique designed to selectively remove correlated errors in the GRACE spectral coefficients. Application of this filter significantly improves the spatial resolution of the GRACE water storage estimates, and produces a time series which agrees quite well (RMS difference = 20.3 mm) with the in situ measurements averaged over an area of ∼280,000 km2.

This study investigates the spatial and temporal variability of the soil moisture in India using Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) gridded datasets from June 2002 to April 2017. Significant relationships between soil moisture and different land surface–atmosphere fields (Precipitation, surface air temperature, total cloud cover, and total water storage) were studied, using maximum covariance analysis (MCA) to extract dominant interactions that maximize the covariance between two fields. The first leading mode of MCA explained 56%, 87%, 81%, and 79% of the squared covariance function (SCF) between soil moisture with precipitation (PR), surface air temperature (TEM), total cloud count (TCC), and total water storage (TWS), respectively, with correlation coefficients of 0.65, −0.72, 0.71, and 0.62. Furthermore, the covariance analysis of total water storage showed contrasting patterns with soil moisture, especially over northwest, northeast, and west coast regions. In addition, the spatial distribution of seasonal and annual trends of soil moisture in India was estimated using a robust regression technique for the very first time. For most regions in India, significant positive trends were noticed in all seasons. Meanwhile, a small negative trend was observed over southern India. The monthly mean value of AMSR soil moisture trend revealed a significant positive trend, at about 0.0158 cm3/cm3 per decade during the period ranging from 2002 to 2017.

This study explores the feasibility of an entirely satellite remote sensing (RS)-based hydrologic budget model for a ground data-constrained basin, the Rufiji basin in Tanzania, from the balance of runoff (Q), precipitation (P), storage change (ΔS), and evapotranspiration (ET). P was determined from the Tropical Rainfall Measuring Mission, ΔS from the Gravity Recovery and Climate Experiment, and ET from the Moderate Resolution Imaging Spectroradiometer, the surface radiation budget, and the Atmosphere Infrared Radiation Sounder. Q was estimated as a residual of the water balance and tested against measured Q for a sub-basin of the Rufiji (the Usangu basin) where ground measurements were available (R2 = 0.58, slope = 1.9, root mean square error = 29 mm/month, bias = 14%). We also tested a geographical information system (GIS)-driven (ArcCN-runoff) runoff model (R2 = 0.64, slope = 0.43, root mean square error = 39 mm/month). We conducted an error propagation analysis from each of the model's hydrologic components (P, ET, and ΔS). We find that the RS-based model amplitude is most sensitive to ET and slightly less so to P, whereas the model's seasonal trends are most sensitive to ∆S. Although RS–GIS-driven models are becoming increasingly used, our results indicate that long-term water resource assessment policy and management may be more appropriate than ‘instantaneous’ or short-term water resource assessment. However, our analyses help develop a series of tools and techniques to progress our understanding of RS–GIS in water resource management of data-constrained basins at the level of a water resource manager. Copyright © 2012 John Wiley & Sons, Ltd.

The terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) is now a significant issue for scientific research in high-resolution time-variable gravity fields. This paper proposes the use of singular spectrum analysis (SSA) to predict the TWSA derived from GRACE. We designed a case study in six regions in China (North China Plain (NCP), Southwest China (SWC), Three-River Headwaters Region (TRHR), Tianshan Mountains Region (TSMR), Heihe River Basin (HRB), and Lishui and Wenzhou area (LSWZ)) using GRACE RL06 data from January 2003 to August 2016 for inversion, which were compared with Center for Space Research (CSR), Helmholtz-Centre Potsdam-German Research Centre for Geosciences (GFZ), Jet Propulsion Laboratory (JPL)’s Mascon (Mass Concentration) RL05, and JPL’s Mascon RL06. We evaluated the accuracy of SSA prediction on different temporal scales based on the correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE), which were compared with that of an auto-regressive and moving average (ARMA) model. The TWSA from September 2016 to May 2019 were predicted using SSA, which was verified using Mascon RL06, the Global Land Data Assimilation System model, and GRACE-FO results. The results show that: (1) TWSA derived from GRACE agreed well with Mascon in most regions, with the highest consistency with Mascon RL06 and (2) prediction accuracy of GRACE in TRHR and SWC was higher. SSA reconstruction improved R, NSE, and RMSE compared with those of ARMA. The R values for predicting TWS in the six regions using the SSA method were 0.34–0.98, which was better than those for ARMA (0.26–0.97), and the RMSE values were 0.03–5.55 cm, which were better than the 2.29–5.11 cm RMSE for ARMA as a whole. (3) The SSA method produced better predictions for obvious periodic and trending characteristics in the TWSA in most regions, whereas the detailed signal could not be effectively predicted. (4) The predicted TWSA from September 2016 to May 2019 were basically consistent with Global Land Data Assimilation System (GLDAS) results, and the predicted TWSA during June 2018 to May 2019 agreed well with GRACE-FO results. The research method in this paper provides a reference for bridging the gap in the TWSA between GRACE and GRACE-FO.

The snow simulations from the recently developed multivariate land snow data assimilation system (SNODAS) for the Northern Hemisphere are assessed with regard to uncertainties in atmospheric forcing, model structure, data assimilation technique, and satellite remote sensing product. The SNODAS consists of the Data Assimilation Research Testbed (DART) and the Community Land Model version 4 (CLM4). A series of experiments are conducted to estimate each of the above uncertainty sources. The experiments include several open‐loop model cases and data assimilation cases that assimilate the snow cover fraction (SCF) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE). The atmospheric forcing uncertainty in terms of precipitation and radiation is found to be the largest among the various uncertainty sources examined, especially over the Tibetan Plateau (TP) and most of the mid‐ and high‐latitudes. Model structure and choice of data assimilation technique are also two big sources of uncertainty in SNODAS. The uncertainty of model structure is represented by two different parameterizations of SCF. The density‐based SCF scheme (as used in CLM4) generally results in better snow simulations than does the stochastic SCF scheme (as in CLM4.5) within the data assimilation framework. The choice of TWS products retrieved from GRACE has relatively the least impact on the snow data assimilation.

The representation of groundwater dynamics in land surface models has received considerable attention in recent years. Most studies have found that soil moisture increases after adding a groundwater component because of the additional supply of water to the root zone. However, the effect of groundwater on land surface hydrologic memory (persistence) has not been explored thoroughly. In this study we investigate the effect of water table dynamics on National Center for Atmospheric Research Community Land Model hydrologic simulations in terms of land surface hydrologic memory. Unlike soil water or evapotranspiration, results show that land surface hydrologic memory does not always increase after adding a groundwater component. In regions where the water table level is intermediate, land surface hydrologic memory can even decrease, which occurs when soil moisture and capillary rise from groundwater are not in phase with each other. Further, we explore the hypothesis that in addition to atmospheric forcing, groundwater variations may also play an important role in affecting land surface hydrologic memory. Analyses show that feedbacks of groundwater on land surface hydrologic memory can be positive, negative, or neutral, depending on water table dynamics. In regions where the water table is shallow, the damping process of soil moisture variations by groundwater is not significant, and soil moisture variations are mostly controlled by random noise from atmospheric forcing. In contrast, in regions where the water table is very deep, capillary fluxes from groundwater are small, having limited potential to affect soil moisture variations. Therefore, a positive feedback of groundwater to land surface hydrologic memory is observed in a transition zone between deep and shallow water tables, where capillary fluxes act as a buffer by reducing high-frequency soil moisture variations resulting in longer land surface hydrologic memory.

The Gravity Recovery and Climate Experiment (GRACE) satellites provide a new quantitative measurement to observe terrestrial water storage (TWS) variations. In this paper, GRACE data were used to detect TWS changes (TWSC) in last decade in the arid region of northwest China. TWSC of the study area were obtained from RL05 Level 2 GRACE data between August 2002 and July 2013. These obtained GRACE based TWSC were thereafter validated against that calculated from global land data assimilation system (GLDAS) data. The validation showed TWSC from two sources were consistent. Together with precipitation, the analyses showed TWSC in this arid region changed over time, and responded sensitively to climate factors. Results indicated that TWSC showed distinct seasonal variation characteristics and the peaks of TWS appeared corresponding to precipitation. The summers of 2005 and 2012 were wet periods with a mean TWS higher than multiple-year average by about 30 mm, while the falls of 2008 and 2009 were dry seasons with a heavily deficit TWS, lower than multiple-year average by about -30 mm. The winter of 2008 was also in deficit but slightly better than the fall of same year. On average, in the last decade, the period of 2008 to 2009 was in the driest condition. Significant decreases in TWS in 2008 and 2009 were successfully detected by GRACE, and corresponded to drought events in the study area. This study showed in the last decade the changes of climate factors resulted in larger TWS variations in the arid region and proved the capability of GRACE in detecting larger-scale and long-term drought events in arid regions.

Terrestrial Water Storage (TWS) in the Bhagirathi Ganga and Vishnu Ganga basins is an important hydrologic component for the Ganga River. Improved estimation of TWS in the Bhagirathi Ganga and Vishnu Ganga basins is crucial for water resources management for India. Despite its importance, storage and change of TWS in the study area has not been well studied at a larger scale. In this investigation, TWS and its change (TWSC) is estimated in the Bhagirathi Ganga and Vishnu Ganga basins during the period of 2004 to 2010. TWS is calculated from two methods: The first method employs the GRACE remote sensing satellite while the second employs estimation by use of climate data of study area provided by NOAA. TWSC is estimated from the TWS by subtraction of two consecutive monthly values. In addition, Total water storage change is also estimated from a water balance approach using a combination of precipitation, runoff and transevaporation. The spatial average values of GRACE and NOAA from 1°×1° spatial resolutions was extracted using a watershed mask derived from the DEM of the Bhagirathi Ganga and Vishnu Ganga basins. The TWS result from the GRACE and NOAA show decreasing trend in TWS of 0.54 cm/year and 0.2 cm/year, respectively due to decrease in precipitation, high evaporation of the study area. The results indicate the great potential of GRACE and water balance approach using NOAA data for providing hydrological information to water resource management in Uttarakhand.