[1] We applied an approach for daily estimation and monitoring of evapotranspiration (ET) over the Northeast Asia monsoon region using satellite remote sensing observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Frequent cloud cover results in a substantial loss of remote sensing information, limiting the capability of continuous ET monitoring for the monsoon region. Accordingly, we applied and evaluated a stand-alone MODIS ET algorithm for representative regional ecosystem types and an alternative algorithm to facilitate continuous regional ET estimates using surface meteorological inputs from the Korea Land Data Assimilation System (KLDAS) in addition to MODIS land products. The resulting ET calculations showed generally favorable agreement (root-mean-square error  < 1.3 mm d−1) with respect to in situ measurements from eight regional flux tower sites. The estimated mean annual ET for 3 years (2006 to 2008) was approximately 362.0 ± 161.5 mm yr−1 over the Northeast Asia domain. In general, the MODIS and KLDAS-based ET (MODIS-KLDAS ET) results showed favorable performance when compared to tower observations, though the results were overestimated for a forest site by approximately 39.5% and underestimated for a cropland site in South Korea by 0.8%. The MODIS-KLDAS ET data were generally underestimated relative to the MODIS (MOD16) operational global terrestrial ET product for various biome types, excluding cropland; however, MODIS-KLDAS ET showed better agreement than MOD16 ET for forest and cropland sites in South Korea. Our results indicate that MODIS ET estimates are feasible but are limited by satellite optical-infrared remote sensing constraints over cloudy regions, whereas alternative ET estimates using continuous meteorological inputs from operational regional climate systems (e.g., KLDAS) provide accurate ET results and continuous monitoring capability under all-sky conditions.

[1] The development of spatially continuous fields from sparse observing networks is an outstanding problem in the environmental and Earth sciences. Here we explore an approach to produce spatially continuous fields from discontinuous data that focuses on reconstructing gaps routinely present in satellite-based Earth observations. To assess the utility of the approach, we use synthetic imagery derived from a regional climate model of southeastern Australia. Orbital tracks, scan geometry influences, and atmospheric artifacts are artificially imposed upon these model simulations to examine the techniques' capacity to reproduce realistic and representative retrievals. The imposed discontinuities are reconstructed using a direct sampling technique and are compared against the original continuous model data: a synthetic simulation experiment. Results indicate that the multipoint geostatistical gap-filling approach produces texturally realistic spatially continuous fields from otherwise discontinuous data sets. Reconstruction results are assessed through comparison of spatial distributions, as well as through visual assessment of fine-scale features. Complex spatial patterns and fine-scale structure can be resolved within the reconstructions, illustrating that the often nonlinear dependencies between variables can be maintained. The stochastic nature of the methodology makes it possible to expand the approach within a Monte Carlo framework in order to estimate the uncertainty related to subsequent reconstructions. From a practical perspective, the reconstruction method is straightforward and requires minimum user intervention for parameter adjustment. As such, it can be automated to systematically process real time remote sensing measurements.

[1] The climate of the Murray-Darling basin (MDB) has been simulated using the Weather Research and Forecasting (WRF) model. WRF was implemented using a 10 km horizontal grid and run for 24 years from 1985 through 2008. The model simulated climate was evaluated against gridded precipitation and temperature observations from the Australian Water Availability Project (AWAP) at daily, monthly, interannual and multiannual time scales. WRF successfully reproduced daily statistics when compared to AWAP observations. It also improves almost all monthly and interannual statistics relative to those of the National Oceanic and Atmospheric Administrations Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis, which supplied the lateral boundary conditions. WRF simulations were able to capture the drought experienced over the basin in recent years, except for an overestimation of the negative anomaly in the northernmost part of the domain. Examining ENSO cycles showed WRF has good skill at capturing the correct spatial distribution of precipitation anomalies associated with El Nino/La Nina events during this 24 year period. The high-resolution simulation developed here allows for improved characterization of land-atmosphere coupling within the basin, including identification of the dominant water vapor source regions for events and seasons and provides insight into the quantification of precipitation recycling.

[1] Terrestrial evapotranspiration (ET) cools the surface and moistens the atmosphere near the Earth's surface. Variations in this important climate factor have major environmental and socioeconomic impacts. How terrestrial ET has varied in the past and what caused the variations, however, have remained quite uncertain. These issues are addressed by calculating monthly global ET from 1982 to 2002 at 1120 globally distributed stations, using a modified Penman-Monteith method that was developed in the first part of the two-part paper. Our analyses show that ET has a significant decadal variation (∼10%) regionally and globally. Over the period analyzed ET for global land increased by 0.6 W m−2 per decade equal to 1.2 W m−2 (about 2.2% in relative value) or 15 mm yr−1 in water flux during the study period. We show that long-term variations of ET in humid areas such as the tropics, Europe, and humid areas of Asia are primarily controlled by variations in incident solar radiation Rs connected to changes in cloudiness and aerosols. However, soil water supply, estimated here by RH, and connected to precipitation, is the dominant factor in controlling long-term variations of ET in arid areas. A correlation analysis demonstrates that the dependence of ET on Rs switches to negative in dry regions. Furthermore, its dependence on relative humidity switches from negative in moist regions to positive in dry regions. Its dependence on normalized difference vegetation index is uniformly positive.

We calculate evapotranspiration (E) from remote sensing (RS) data using the Penman–Monteith model over continental USA for four years (2003–2006) and explore, through an ensemble generation framework, the impact of input dataset (meteorological, radiation and vegetation) selection on performance (uncertainty) at the monthly time-scale. The impact of failed or missed RS retrievals and algorithmic assumptions are also quantified. To evaluate bias, we inter-compare RS-E with three independent sources of E: Variable Infiltration Capacity (VIC)-model simulated, North American Regional Reanalysis (NARR) inferred, and Gravity Recovery and Climate Experiment (GRACE) inferred. Overall, we find that the choice of vegetation parameterization, followed by surface temperature, has the greatest impact on RS-E uncertainty. Additional uncertainty (4–18%) is linked to sources of net radiation—used to scale instantaneous RS-E under the assumption of constant daytime evaporative fraction—including the Surface Radiation Budget (SRB), International Satellite Cloud Climatology Project (ISCCP), and North American Land Data Assimilation System (NLDAS)-VIC. The ensemble median agrees to within 21% of VIC-modelled E, except for the Colorado and Great Basins for which the need for a soil moisture constraint on RS-E becomes evident.

We applied a satellite remote sensing–based evapotranspiration (ET) algorithm to assess global terrestrial ET from 1983 to 2006. The algorithm quantifies canopy transpiration and soil evaporation using a modified Penman‐Monteith approach with biome‐specific canopy conductance determined from the normalized difference vegetation index (NDVI) and quantifies open water evaporation using a Priestley‐Taylor approach. These algorithms were applied globally using advanced very high resolution radiometer (AVHRR) GIMMS NDVI, NCEP/NCAR Reanalysis (NNR) daily surface meteorology, and NASA/GEWEX Surface Radiation Budget Release−3.0 solar radiation inputs. We used observations from 34 FLUXNET tower sites to parameterize an NDVI‐based canopy conductance model and then validated the global ET algorithm using measurements from 48 additional, independent flux towers. Two sets of monthly ET estimates at the tower level, driven by in situ meteorological measurements and meteorology interpolated from coarse resolution NNR meteorology reanalysis, agree favorably (root mean square error (RMSE) = 13.0–15.3 mm month−1; R2 = 0.80–0.84) with observed tower fluxes from globally representative land cover types. The global ET results capture observed spatial and temporal variations at the global scale and also compare favorably (RMSE = 186.3 mm yr−1; R2 = 0.80) with ET inferred from basin‐scale water balance calculations for 261 basins covering 61% of the global vegetated area. The results of this study provide a relatively long term global ET record with well‐quantified accuracy for assessing ET climatologies, terrestrial water, and energy budgets and long‐term water cycle changes.

Water resources on Earth become one of the main concerns for society. Therefore, remote sensing methods are still under development in order to improve the picture of the global water cycle. In this context, the microwave bands are the most suitable to study land–water resources. The Soil Moisture and Ocean Salinity (SMOS), satellite mission of the European Space Agency (ESA), is dedicated for studies of the water in soil over land and salinity of oceans. The part of calibration/validation activities in order to improve soil moisture retrieval algorithms over land is done with ground-based passive radiometers. The European Space Agency L-band Microwave Radiometer (ELBARA III) located near the Bubnów wetland in Poland is capable of mapping microwave emissivity at the local scale, due to the azimuthal and vertical movement of the horn antenna. In this paper, we present results of the spatio-temporal mapping of the brightness temperatures on the heterogeneous area of the Bubnów test-site consisting of an area with variable organic matter (OM) content and different type of vegetation. The soil moisture (SM) was retrieved with the L-band microwave emission of the biosphere (L-MEB) model with simplified roughness parametrization (SRP) coupling roughness and optical depth parameters. Estimated soil moisture values were compared with in-situ data from the automatic agrometeorological station. The results show that on the areas with a relatively low OM content (4–6%—cultivated field) there was good agreement between measured and estimated SM values. Further increase in OM content, starting from approximately 6% (meadow wetland), caused an increase in bias, root mean square error (RMSE), and unbiased RMSE (ubRMSE) values and a general drop in correlation coefficient (R). Despite a span of obtained R values, we found that time-averaged estimated SM using the L-MEB SRP approach strongly correlated with OM contents.

Water resources management (WRM) for sustainable development presents many challenges in areas with sparse in situ monitoring networks. The exponential growth of satellite based information over the past decade provides unprecedented opportunities to support and improve WRM. Furthermore, traditional barriers to the access and usage of satellite data are lowering as technological innovations provide opportunities to manage and deliver this wealth of information to a wider audience. We review data needs for WRM and the role that satellite remote sensing can play to fill gaps and enhance WRM, focusing on the Latin American and Caribbean as an example of a region with potential to further develop its resources and mitigate the impacts of hydrological hazards. We review the state-of-the-art for relevant variables, current satellite missions, and products, how they are being used currently by national agencies across the Latin American and Caribbean region, and the challenges to improving their utility. We discuss the potential of recently launched, upcoming, and proposed missions that are likely to further enhance and transform assessment and monitoring of water resources. Ongoing challenges of accuracy, sampling, and continuity still need to be addressed, and further challenges related to the massive amounts of new data need to be overcome to best leverage the utility of satellite based information for improving WRM.

To address three important issues related to extraction of water features from Landsat imagery, i.e., selection of water indexes and classification algorithms for image classification, collection of ground truth data for accuracy assessment, this study applied four sets (ultra-blue, blue, green, and red light based) of water indexes (NWDI, MNDWI, MNDWI2, AWEIns, and AWEIs) combined with three types of image classification methods (zero-water index threshold, Otsu, and kNN) to 24 selected lakes across the globe to extract water features from Landsat-8 OLI imagery. 1440 (4x5x3x24) image classification results were compared with the extracted water features from high resolution Google Earth images with the same (or 1 day) acquisition dates through computing the Kappa coefficients. Results show the kNN method is better than the Otsu method, and the Otsu method is better than the zero-water index threshold method. If the computational cost is not an issue, the kNN method combined with the ultra-blue light based AWEIns is the best method for extracting water features from Landsat imagery because it produced the highest Kappa coefficients. If the computational cost is taken into account, the Otsu method is a good choice. AWEIns and AWEIs are better than NDWI, MNDWI and MNDWI2. AWEIns works better than AWEIs under the Otsu method, and the average rank of the image classification accuracy from high to low is the ultra-blue, blue, green, and red light-based AWEIns.

This study assessed the long-term (1979–2008) water budget closures for 19 large cold region drainage basins in Canada using recently developed datasets for precipitation (P), land surface evapotranspiration and water surface evaporation, and observed streamflow. Total water storage (TWS) trends from the GRACE satellite observations were also used to assist the assessment. The objectives are to quantify the magnitudes and spatial patterns of the water budget imbalance (e) and its source of errors for these cold region basins. Results showed that the water budget was closed within 10% of the P on average for all the basins. The e showed a general pattern of positive values in the south and negative values in the north and mountainous regions over the country. Basins with large e values were mostly found in the north. Uncertainties in the water budget variables, particularly P, were found to play a major role in the e. Significant trends in TWS were found over 11 basins, which accounted for 31% of their e on average. Improvements in the observation network, data quality assurance, and spatial models for P are critical for further improving the water budget closure for the cold region drainage basins. © 2014 Her Majesty the Queen in Right of Canada. Hydrological Processes. © John Wiley & Sons, Ltd.

This paper reconciles the state‐of‐the‐art observations and simulations of evapotranspiration (ET) temporal variability through a diagnostic framework composed of an observation‐model‐theory triplet. Specifically, a confirmed theoretical tool, Evapotranspiration Temporal VARiance Decomposition (EVARD), is used as a benchmark to estimate ET monthly variance ( σET2 ) across the contiguous United States (CONUS) with inputs including hydroclimatic observations, Gravity Recovery and Climate Experiment‐based terrestrial water storage, four observation‐based products (ETRSUW by the University of Washington, ETRSMOD16 from MOD16 Global Terrestrial ET Data Set, ETFLUXNET upscaled from of fluxtower observations, and ETGLEAM from Global Land Evaporation Amsterdam Model), and four operational land surface models (LSMs: MOSAIC, NOAH, NOAH‐MP, and VIC). Five experiments are systematically designed to evaluate and diagnose possible errors and uncertainties in ET temporal variance estimated by the four observation‐based ET products and the four LSM simulations. Based on the results of these experiments, the following diagnostic hypotheses regarding the uncertainty of the observation‐based ET products are illustrated: ETRSUW captures the high σET2 signals in the Midwest with negligible bias and moderate uncertainty over the contiguous United States; ETFLUXNET systematically underestimates σET2 over CONUS but with the lowest level of uncertainty; ETRSMOD16 has medium bias with the highest level of uncertainty, and the spatial distribution of high σET2 signal from ETRSMOD16 is different from other estimates; ETGLEAM has slight negative bias and medium uncertainty, and σET2 in the West Coast is smaller than that from ETVARD. Regarding the LSMs, it is found that any of the four LSMs can be the best depending on a certain set of reference observations. The study reveals that LSMs have shown a reasonably worthy, though not perfect, capability in estimating ET and its variability in regions/aquifers with limited human interference. However, RS‐based observations and theoretical estimates suggest that all the four LSMs examined in this study are not able to accurately predict the ET variability in regions/aquifers heavily influenced by human activities like Central Valley and High Plains aquifers; they all underestimate ET variability along the West Coast due to seasonal vegetation responses to Mediterranean climate and human water use. In addition, LSMs underestimate intraannual ET variance in California and the High Plains with underestimated terrestrial storage change components in ET variance, due to the inappropriate representation of groundwater pumping and its impact on ET and other hydrologic processes. This paper urges advancing hydrologic knowledge by finding congruence among models, data, and theories.

The role of atmospheric general circulation model (AGCM) horizontal resolution in representing the global energy budget and hydrological cycle is assessed, with the aim of improving the understanding of model uncertainties in simulating the hydrological cycle. We use two AGCMs from the UK Met Office Hadley Centre: HadGEM1-A at resolutions ranging from 270 to 60 km, and HadGEM3-A ranging from 135 to 25 km. The models exhibit a stable hydrological cycle, although too intense compared to reanalyses and observations. This over-intensity is explained by excess surface shortwave radiation, a common error in general circulation models (GCMs). This result is insensitive to resolution. However, as resolution is increased, precipitation decreases over the ocean and increases over the land. This is associated with an increase in atmospheric moisture transport from ocean to land, which changes the partitioning of moisture fluxes that contribute to precipitation over land from less local to more non-local moisture sources. The results start to converge at 60-km resolution, which underlines the excessive reliance of the mean hydrological cycle on physical parametrization (local unresolved processes) versus model dynamics (large-scale resolved processes) in coarser HadGEM1 and HadGEM3 GCMs. This finding may be valid for other GCMs, showing the necessity to analyze other chains of GCMs that may become available in the future with such a range of horizontal resolutions. Our finding supports the hypothesis that heterogeneity in model parametrization is one of the underlying causes of model disagreement in the Coupled Model Intercomparison Project (CMIP) exercises.

The process whereby the spatially distributed runoff (generated through saturation/infiltration excesses, subsurface flow, etc.) travels over the hillslope and river net- work and becomes streamflow is generally referred to as "routing". In short, routing is a runoff-to-streamflow process, and the streamflow in rivers is the response to runoff inte- grated in both time and space. Here we develop a method- ology to invert the routing process, i.e., to derive the spa- tially distributed runoff from streamflow (e.g., measured at gauge stations) by inverting an arbitrary linear routing model using fixed interval smoothing. We refer to this streamflow- to-runoff process as "inverse routing". Inversion experiments are performed using both synthetically generated and real streamflow measurements over the Ohio River basin. Re- sults show that inverse routing can effectively reproduce the spatial field of runoff and its temporal dynamics from suf- ficiently dense gauge measurements, and the inversion per- formance can also be strongly affected by low gauge density and poor data quality. The runoff field is the only component in the terrestrial water budget that cannot be directly measured, and all pre- vious studies used streamflow measurements in its place. Consequently, such studies are limited to scales where the spatial and temporal difference between the two can be ig- nored. Inverse routing provides a more sophisticated tool than traditional methods to bridge this gap and infer fine- scale (in both time and space) details of runoff from aggre- gated measurements. Improved handling of this final gap in terrestrial water budget analysis may potentially help us to use space-borne altimetry-based surface water measurements for cross-validating, cross-correcting, and assimilation with other space-borne water cycle observations.

The increasing availability and reliability of satellite remote sensing products [e.g., precipitation (P), evapotranspiration (ET), and the total water storage change (TWSC)] make it feasible to estimate the global terrestrial water budget at fine spatial resolution. In this study, we start from a reference water budget dataset that combines all available data sources, including satellite remote sensing, land surface model (LSM) and reanalysis, and investigate the roles of different non-satellite remote sensing products in closing the terrestrial water budget through a sensitivity analysis by removing/replacing one or more categories of products during the budget estimation. We also study the differences made by various satellite products for the same budget variable. We find that the gradual removal of non-satellite data sources will generally worsen the closure errors in the budget estimates, and remote sensing retrievals of P, ET, and TWSC together with runoff (R) from LSM give the worst closure errors. The gauge-corrected satellite precipitation helps to improve the budget closure (4.2–9 % non-closure errors of annual mean precipitation) against using the non-gauge-corrected precipitation (7.6–10.4 % non-closure errors). At last, a data assimilation technique, the constrained Kalman filter, is applied to enforce the water balance, and it is found that the satellite remote sensing products, though with worst closure, yield comparable budget estimates in the constrained system to the reference data. Overall, this study provides a first comparison between the water budget closure using the satellite remote sensing products and a full combination of remote sensing, LSM, and reanalysis products on a quasi-global basis. This study showcases the capability and potential of the satellite remote sensing in closing the terrestrial water budget at fine spatial resolution if properly constrained.

The Three Gorges Project (TGP) has received many criticisms about its potential effects on the changes in the downstream ecosystems. Poyang Lake is the largest body of water downstream of the TGP, and it is not immune to these changes. TRMM and GRACE data were introduced in this study to estimate river-lake water exchange, from which the hydrological responses of Poyang Lake could be identified. A significant decreasing trend of the runoff coefficient has been observed since 2003, resulting in 6.02 km3 more water discharge from the lake into the Yangtze River than under normal conditions. No significant interannual changes occurred in the water level or local precipitation, and GRACE observations revealed that groundwater discharge appeared to be the most likely compensation for the water loss. A novel approach, namely, the groundwater abnormality index (GAI), was developed to depict the water exchange using GRACE and surface water observations. Lower than normal GAIs were found between 2003 and 2005, reaching a minimum of −29.26 in October 2003, corresponding to ten times of the mean GAI during 2006–2012, clearly indicating a significant water exchange in Poyang Lake Basin from groundwater to surface water after the TGP impoundment.

The Gravity Recovery and Climate Experiment (GRACE) mission ended its operation in October 2017, and the GRACE Follow‐On mission was launched only in May 2018, leading to approximately 1 year of data gap. Given that GRACE‐type observations are exclusively providing direct estimates of total water storage change (TWSC), it would be very important to bridge the gap between these two missions. Furthermore, for many climate‐related applications, it is also desirable to reconstruct TWSC prior to the GRACE period. In this study, we aim at comparing different data‐driven methods and identifying the more robust alternatives for predicting GRACE‐like gridded TWSC during the gap and reconstructing them to 1992 using climate inputs. To this end, we first develop a methodological framework to compare different methods such as the multiple linear regression (MLR), artificial neural network (ANN), and autoregressive exogenous (ARX) approaches. Second, metrics are developed to measure the robustness of the predictions. Finally, gridded TWSC within 26 regions are predicted and reconstructed using the identified methods. Test computations suggest that the correlation of predicted TWSC maps with observed ones is more than 0.3 higher than TWSC simulated by hydrological models, at the grid scale of 1° resolution. Furthermore, the reconstructed TWSC correctly reproduce the El Nino‐Southern Oscillation (ENSO) signals. In general, while MLR does not perform best in the training process, it is more robust and could thus be a viable approach both for filling the GRACE gap and for reconstructing long‐period TWSC fields globally when combined with statistical decomposition techniques.

The Arctic freshwater cycle is changing rapidly, which will require adequate monitoring of river flows to detect, observe, and understand changes and provide adaptation information. There has, however, been little detail about where the greatest flow changes are projected, and where monitoring therefore may need to be strengthened. In this study, we used a set of recent climate model runs and an advanced macro-scale hydrological model to analyze how flows across the continental pan-Arctic are projected to change and where the climate models agree on significant changes. We also developed a method to identify where monitoring stations should be placed to observe these significant changes, and compared this set of suggested locations with the existing network of monitoring stations. Overall, our results reinforce earlier indications of large increases in flow over much of the Arctic, but we also identify some areas where projections agree on significant changes but disagree on the sign of change. For monitoring, central and eastern Siberia, Alaska, and central Canada are hot spots for the highest changes. To take advantage of existing networks, a number of stations across central Canada and western and central Siberia could form a prioritized set. Further development of model representation of high-latitude hydrology would improve confidence in the areas we identify here. Nevertheless, ongoing observation programs may consider these suggested locations in efforts to improve monitoring of the rapidly changing Arctic freshwater cycle.

Surface water storage (SWS), the amount of freshwater stored in rivers/wetlands/floodplains/lakes, and its variations are key components of the water cycle and land surface hydrology, with strong feedback and linkages with climate variability. They are also very important for water resources management. However, it is still very challenging to measure and to obtain accurate estimates of SWS variations for large river basins at adequate time/space sampling. Satellite observations offer great opportunities to measure SWS changes, and several methods have been developed combining multisource observations for different environments worldwide. With the upcoming launch in 2022 of the Surface Water and Ocean Topography (SWOT) satellite mission, which will provide, for the first time, direct estimates of SWS variations with an unprecedented spatial resolution (~100 m), it is timely to summarize the recent advances in the estimates of SWS from satellite observations and how they contribute to a better understanding of large-scale hydrological processes. Here, we review the scientific literature and present major results regarding the dynamic of surface freshwater in large rivers, floodplains, and wetlands. We show how recent efforts have helped to characterize the variations in SWS change across large river basins, including during extreme climatic events, leading to an overall better understanding of the continental water cycle. In the context of SWOT and forthcoming SWS estimates at the global scale, we further discuss new opportunities for hydrological and multidisciplinary sciences. We recommend that, in the near future, SWS should be considered as an essential water variable to ensure its long-term monitoring.

Summary This study represents the first attempt to examine the spatial and seasonal variations of the surface water budget by using state-of-the-art datasets for sixteen large Canadian drainage basins with a total area of 3.2 million km2. The datasets used include two precipitation grids produced using measurements and reanalysis models, land surface evapotranspiration and water surface evaporation estimated using the EALCO model, streamflow measured at hydrometric stations, and total water storage change derived from GRACE satellite observations. The monthly water imbalance resulted from these datasets varied from 7.0 mm month−1 to 21 mm month−1 among the studied basins, which was 30% on average of the corresponding monthly precipitation. The accumulated water budget imbalance over the 7 years of 2002–2008 varied from close to zero to ±10 mm month−1. The positive and negative imbalances among the sixteen basins were largely offset and the all-basin imbalance was very close to 0. The uncertainties in precipitation, streamflow, evapotranspiration and total water storage change all contributed to the water budget imbalance and their relative magnitudes were found to vary with basin and season. In most cases, precipitation showed the largest uncertainties, which had similar magnitudes to the water budget imbalances. While improvements are noted in comparison with previous water budget studies over the regions, the water imbalance obtained for some basins is quite large, suggesting that considerable improvements in both the observation networks and models are necessary before the water budget closure can be substantially improved over this region.

Summary The goal of this research is to quantify variations in both space and time of water stored in the terrestrial environment within South Carolina during and following a period of drought. We use a water balance approach that synthesizes observed and modeled hydrologic fluxes for sub-watersheds defined by the drainage area between streamflow gaging stations. We apply the approach for the period 1998–2007 to study the impact of a drought that occurred during the early part of this time period on terrestrial water storage within the state. Results from the analysis provide evidence of distinct annual and interannual variation in water storage for different regions of the state, with the fall season having a water surplus and spring season exhibiting a water deficit. The impact of the drought varied for different regions of the state depending in part on hydrogeological conditions including soil type and depth to the groundwater level. Comparing estimates of rate of change in terrestrial water storage with observed groundwater levels, as an independent validation of the terrestrial water storage estimations, shows that many of the sub-watersheds within the state exhibited similar patterns between variation of rate of change in terrestrial water storage estimates and observed groundwater levels during the period of analysis, as expected. However, some sub-watersheds did not follow general annual and interannual variations in groundwater level or in estimated rate of change in terrestrial water storage relative to neighboring sub-watersheds. We speculate that these abnormalities may be related to human influences that alter local water storage trends within specific sub-watersheds of the state, however future work is needed to further investigate this possible explanation. We conclude through this study that the water balance approach presented is a simple yet valuable means for estimating variations in water availability at a regional spatial scale by synthesizing existing observations and model output data within a geospatially-explicit context.