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The seasonal-to-decadal terrestrial water balance on river basin scales depends on several well-characterized but uncertain soil physical processes, including soil moisture, plant available water, rooting depth, and recharge to lower soil layers. Reducing uncertainties in these quantities using observations is a key step toward improving the data fidelity and skill of land surface models. In this study, we quantitatively characterize the capability of Gravity Recovery and Climate Experiment (NASA-GRACE) measurements-a key constraint on total water storage (TWS)-to inform and constrain these processes. We use a reduced-complexity physically based model capable of simulating the hydrologic cycle, and we apply Bayesian inference on the model parameters using a Markov chain Monte Carlo algorithm, to minimize mismatches between model-simulated and GRACE-observed TWS anomalies. Based on the prior and posterior model parameter distributions, we further quantify information gain with regard to terrestrial water states, associated fluxes, and time-invariant process parameters. We show that the data-constrained terrestrial water storage model can capture basic physics of the hydrologic cycle for a watershed in the western Amazon during the period January 2003 through December 2012, with an r2 of 0.98 and root mean square error of 30.99 mm between observed and simulated TWS. Furthermore, we show a reduction of uncertainty in many of the parameters and state variables, ranging from a 2 % reduction in uncertainty for the porosity parameter to an 85 % reduction for the rooting depth parameter. The annual and interannual variability of the system are also simulated accurately, with the model simulations capturing the impacts of the 2005-2006 and 2010-2011 South American droughts. The results shown here suggest the potential of using gravimetric observations of TWS to identify and constrain key parameters in soil hydrologic models.

Progress towards achieving a quantitative understanding of the exchanges of water between Earth's main water reservoirs is reviewed with emphasis on advances accrued from the latest advances in Earth Observation from space. These exchanges of water between the reservoirs are a result of processes that are at the core of important physical Earth-system feedbacks, which fundamentally control the response of Earth's climate to the greenhouse gas forcing it is now experiencing, and are therefore vital to understanding the future evolution of Earth's climate. The changing nature of global mean sea level (GMSL) is the context for discussion of these exchanges. Different sources of satellite observations that are used to quantify ice mass loss and water storage over continents, how water can be tracked to its source using water isotope information and how the waters in different reservoirs influence the fluxes of water between reservoirs are described. The profound influence of Earth's hydrological cycle, including human influences on it, on the rate of GMSL rise is emphasized. The many intricate ways water cycle processes influence water exchanges between reservoirs and thus sea-level rise, including disproportionate influences by the tiniest water reservoirs, are emphasized.

Time-resolved satellite gravimetry has revolutionized understanding of mass transport in the Earth system. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) has enabled monitoring of the terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure variations and understanding responses to changes in the global climate system. Initially a pioneering experiment of geodesy, the time-variable observations have matured into reliable mass transport products, allowing assessment and forecast of a number of important climate trends and improve service applications such as the U.S. Drought Monitor. With the successful launch of the GRACE Follow-On mission, a multi decadal record of mass variability in the Earth system is within reach.

Ten years of terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) were used to estimate high latitude snowfall accumulation using a mass balance approach. The estimates were used to assess two common gauge-undercatch correction factors (CFs): Legates climatology (CF-L) utilized in the Global Precipitation Climatology Project (GPCP), and Fuchs dynamic correction model (CF-F) used in the Global Precipitation Climatology Centre (GPCC) Monitoring product. The two CFs can be different by more than 50%. CF-L tended to exceed CF-F over northern Asia and Eurasia, while the opposite was observed over North America. Estimates of snowfall from GPCP, GPCC-L (GPCC corrected by CF-L), and GPCC-F (GPCC corrected by CF-F) were 62%, 64%, and 46% more than GPCC over northern Asia and Eurasia. GRACE-based estimate (49% more than GPCC) was the closest to GPCC-F. We found that as near surface air temperature decreases, the products increasingly underestimated the GRACE-based snowfall accumulation. Overall, GRACE showed that CFs are effective in improving GPCC estimates. Furthermore, our case studies and overall statistics suggest that CF-F is likely more effective than CF-L in most of the high latitude regions studied here. GPCP showed generally better skill than GPCC-L, which might be related to the use of satellite data or additional quality controls on gauge inputs to GPCP. This study suggests that GPCP can be improved if it employs CF-L instead of CF-F to correct for gauge undercatch. However, this implementation requires further studies, region-specific analysis, and operational considerations.

Accurate quantification of global land evapotranspiration is necessary for understanding variability in the global water cycle, which is expected to intensify under climate change.sup.1-3. Current global evapotranspiration products are derived from a variety of sources, including models.sup.4,5, remote sensing.sup.6,7 and in situ observations.sup.8-10. However, existing approaches contain extensive uncertainties; for example, relating to model structure or the upscaling of observations to a global level.sup.11. As a result, variability and trends in global evapotranspiration remain unclear.sup.12. Here we show that global land evapotranspiration increased by 10 [plus or minus] 2 per cent between 2003 and 2019, and that land precipitation is increasingly partitioned into evapotranspiration rather than runoff. Our results are based on an independent water-balance ensemble time series of global land evapotranspiration and the corresponding uncertainty distribution, using data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) satellites.sup.13. Variability in global land evapotranspiration is positively correlated with El Niño-Southern Oscillation. The main driver of the trend, however, is increasing land temperature. Our findings provide an observational constraint on global land evapotranspiration, and are consistent with the hypothesis that global evapotranspiration should increase in a warming climate. Using a global mass-balance approach to calculate evapotranspiration, it is shown that global land evapotranspiration increased by 10% between 2003 and 2019, driven mainly by warming land temperatures., Author(s): Madeleine Pascolini-Campbell [sup.1] , John T. Reager [sup.1] , Hrishikesh A. Chandanpurkar [sup.1] , Matthew Rodell [sup.2] Author Affiliations: (1) NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, [...]

Abstract The widespread influence of reservoirs on global rivers makes representations of reservoir outflow and storage essential components of large-scale hydrology and climate simulations across the land surface and atmosphere. Yet, reservoirs have yet to be commonly integrated into earth system models. This deficiency influences model processes such as evaporation and runoff, which are critical for accurate simulations of the coupled climate system. This study describes the development of a generalized reservoir model capable of reproducing realistic reservoir behavior for future integration in a global land surface model (LSM). Equations of increasing complexity relating reservoir inflow, outflow, and storage were tested for 14 California reservoirs that span a range of spatial and climate regimes. Temperature was employed in model equations to modulate seasonal changes in reservoir management behavior and to allow for the evolution of management seasonality as future climate varies. Optimized parameter values for the best-performing model were generalized based on the ratio of winter inflow to storage capacity so a future LSM user can generate reservoirs in any grid location by specifying the given storage capacity. Model performance statistics show good agreement between observed and simulated reservoir storage and outflow for both calibration (mean normalized RMSE = 0.48; mean coefficient of determination = 0.53) and validation reservoirs (mean normalized RMSE = 0.15; mean coefficient of determination = 0.67). The low complexity of model equations that include climate-adaptive operation features combined with robust model performance show promise for simulations of reservoir impacts on hydrology and climate within an LSM.

Renewable groundwater stress is quantified in the world's largest aquifersCharacteristic stress regimes are defined to determine the severity of stressOverstressed aquifers are mainly in rangeland biomes with some croplands.

Soil water storage is a fundamental signal in the land hydrological cycle and changes in soil moisture can affect regional climate. In this study, we used simulations from Coupled Model Intercomparison Project Phase 5 archives to investigate changes in the annual range of soil water storage under global warming at northern middle and high latitudes. Results show that future warming could lead to significant declines in snowfall, and a corresponding lack of snowmelt water recharge to the soil, which makes soil water less available during spring and summer. Conversely, more precipitation as rainfall results in higher recharge to soil water during its accumulating season. Thus, the wettest month of soil water gets wetter, and the driest month gets drier, resulting in an increase of the annual range and suggesting that stronger heterogeneity in global water distribution (changing extremes) could occur under global warming; this has implications for water management and water security under a changing climate.

We evaluate performance of the Catchment Land Surface Model (CLSM) under flood conditions after the assimilation of observations of the terrestrial water storage anomaly (TWSA) from NASA's Gravity Recovery and Climate Experiment (GRACE). Assimilation offers three key benefits for the viability of GRACE observations to operational applications: (1) near-real time analysis; (2) a downscaling of GRACE's coarse spatial resolution; and (3) state disaggregation of the vertically-integrated TWSA. We select the 2011 flood event in the Missouri river basin as a case study, and find that assimilation generally made the model wetter in the months preceding flood. We compare model outputs with observations from 14 USGS groundwater wells to assess improvements after assimilation. Finally, we examine disaggregated water storage information to improve the mechanistic understanding of event generation. Validation establishes that assimilation improved the model skill substantially, increasing regional groundwater anomaly correlation from 0.58 to 0.86. For the 2011 flood event in the Missouri river basin, results show that groundwater and snow water equivalent were contributors to pre-event flood potential, providing spatially-distributed early warning information.

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.

We present a quantitative approach for measuring hydrological drought occurrence and severity based on terrestrial water storage observations from NASA's Gravity Recovery and Climate Experiment (GRACE) satellite mission. GRACE measurements are applied by calculating the magnitude of the deviation of regional, monthly terrestrial water storage anomalies from the time series' monthly climatology, where negative deviations represent storage deficits. Monthly deficits explicitly quantify the volume of water required to return to normal water storage conditions. We combine storage deficits with event duration to calculate drought severity. Drought databases are referenced to identify meteorological drought events in the Amazon and Zambezi River basins and the southeastern United States and Texas regions. This storage deficit method clearly identifies hydrological drought onset, end, and duration; quantifies instantaneous severity and peak drought magnitude; and compares well with the meteorological drought databases. It also reveals information about the hydrological effects of meteorological drought on regional water storage. Key Point GRACE-based drought severity using water storage rather than just precipitation explicitly quantifies the volume of water needed to return to normal conditions and identifies hydrological drought onset, peak magnitude, duration, and severity ©2014. American Geophysical Union. All Rights Reserved.

Extreme hydrological events (including droughts and floods) produce severe social and economic impacts. Monitoring hydrological processes from remote sensing is necessary to improve understanding and preparedness for these events, with current missions focusing on a range of hydrological variables (i.e., SWOT, SMAP, and GRACE). This study uses output from three state-of-the-art land surface assimilation models and an event clustering algorithm to identify the characteristic spatial and temporal scales of large-scale extreme dry and wet events in the contiguous United States for three major hydrological processes: precipitation, runoff, and soil moisture. We also examine the sensitivity of extreme event characteristics to model resolution and assess intermodel differences. We find that models generally agree in terms of the mean characteristics of events: precipitation dry events are of shorter duration in comparison with soil moisture and runoff events, and more intense events tend to be smaller in area. We also find that mean spatial and temporal characteristics are highly dependent on model resolution—important in the context of detecting and monitoring these events. Results from this study can be used to inform land surface model development, extreme hydrology event detection, and sampling requirements of upcoming remote sensing missions in hydrology. Significance Statement: Understanding the fundamental characteristics of dry and wet extreme events (droughts and floods) is of importance for improving our preparedness and response to events, as well as for designing satellite observing systems that can adequately monitor them. Here we use output from land surface models to determine the average size and duration of large-scale extreme events for the contiguous United States using fine temporal data. We find that events that are most extreme—the most severe floods and droughts—tend to be shorter in duration and smaller in size. We also present an assessment of how three commonly used land surface models detect extreme hydrological events, which is important for assessments based on models. These findings are important for understanding the proportion of events that may be not adequately resolved by current hydrology remote sensing missions. [ABSTRACT FROM AUTHOR]