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To compensate for the coarse resolution of groundwater storage (GWS) estimation by the Gravity Recovery and Climate Experiment (GRACE) satellites and make better use of available observed groundwater‐level (GWL) data in some aquifers, a ground‐based scaling factor (SF) method is proposed here to derive high‐resolution GRACE GWS estimates. Improvement is achieved by using the gridded SF derived from assimilating ground‐based GWL observations. The proposed SF method is tested on the North China Plain (NCP, ∼140,000 km2), where a dense network of observation wells and a consistently estimated specific yield (SY) data set are available, to demonstrate its effectiveness and practical applications. The sensitivities of SF‐estimated GWS accuracy to the specification of SY and the assimilation of GWL observation data are explored through four designed numerical experiments. Results show that this novel ground‐based method can reduce the impact of SY uncertainty on GWS estimates, particularly in regions with more pronounced regional GWS trends. The accuracy of SF‐estimated GWS is primarily determined by whether the assimilated wells can reflect the regionally averaged GWS trend. GWS accuracy is less dependent on the number of available wells assimilated. The estimated GWS trend (2004–2015) in NCP is −32.6 ± 1.3 mm/yr (−4.6 ± 0.2 km3/yr), with contrasting GWS trends found in the west Piedmont Plain (∼54,000 km2, with a loss of −66.8 mm/yr) and the coastal Eastern Plain (∼20,000 km2, and a gain of +7.2 mm/yr). Despite the limitations of regional and time scale dependence inherent in SF method, this study highlights the benefits of assimilating in situ observed GWL data instead of using model simulations in estimating SF to downscale GRACE GWS to the higher‐resolution that is desired by local water resources management. Key Points: A new downscaling method based on groundwater‐based scaling factor correction is proposed to improve gravity recovery and climate experiment (GRACE) groundwater storage estimatesThe proposed method can reduce the impact of specific yield uncertainty on groundwater storage (GWS) estimation due to the GWS information provided by the assimilated wells of groundwater levelThe accuracy of GWS change based on the proposed method is primarily determined by whether the assimilated wells can reflect the regional‐averaged GWS trend, less dependent on the number of available wells assimilated [ABSTRACT FROM AUTHOR]

The Huaihe River basin (HRB) of China located in the climate transition zone between warm temperate and subtropical areas is highly sensitive to climatic change. However, the changes in future climate extreme events under anthropogenic warming and the population exposure to these climate extremes in HRB remain unexplored. Here, using the eight commonly used extreme climate indices and based on the bias‐corrections of 16 global climate models (GCMs) in CMIP6, we present a projection and uncertainty analysis of extreme events and investigate the corresponding population exposure risk in HRB under three shared socioeconomic pathways (SSP1‐2.6, SSP2‐4.5, SSP5‐8.5). The 16‐GCM ensemble mean projects an evident warming trend under all three scenarios with a total increase of 25.6–68.0 days in summer days (>25°C) by the end of the century in HRB. Larger increases (decreases) in maximum and minimum temperatures (frost days) are projected in the western HRB. Very heavy rain days (R20mm), maximum 5‐day precipitation (RX5day) and simple daily intensity index (SDII) will experience intensification across most of HRB (especially in southern and western HRB). The consecutive dry days is projected to decrease in northwestern HRB and increase in southern HRB. However, there is a large spatial variability in GCM uncertainty with a higher SSP scenario generally having higher uncertainty. Increases in summer days and R20mm exacerbate population exposure in HRB in near future (2030–2059), but in far future (2070–2099) although summer days (R20mm) continues to rise, population exposure is expected to decrease due to the rapid decline in population density. [ABSTRACT FROM AUTHOR]

Groundwater storage anomaly (GWSA) can be estimated either at the large scale from the Gravity Recovery and Climate Experiment (GRACE) or at the local scale based on in situ observed groundwater level (GWL) and aquifer storage parameters. Yet, the accuracy of GRACE‐based estimate is affected by leakage errors, while that of local GWL‐based estimate requires the reliable specific yield (Sy) data that are usually not available. Here, we developed a novel approach, the coordinated forward modeling (CoFM), based on the iterative forward modeling to improve GWSA estimation at the sub‐regional scale smaller than the typical GRACE footprint. It is achieved by solving Sy through iterative comparisons between GRACE‐based and observation‐based GWSA at 0.5° grid scale, and then re‐calculating GWSA using the updated Sy and observed GWL. The utility of CoFM is explored by using the hypothetical experiments and a real case study in the Piedmont Plain (PP, ∼54,000 km2) and East‐central Plain (ECP, ∼86,000 km2) of North China Plain. Results show that CoFM can detect GWSA at 0.5° grid scale in the hypothetical experiments given the large spatial variability of GWL. While in the real case study, the CoFM distinguishes between the divergent unconfined GWSA trends (2005–2016) in PP (−41.80 ± 0.55 mm/yr) and ECP (−7.57 ± 0.60 mm/yr) caused by the differences in hydrogeological conditions and groundwater use. The improvement made by CoFM can be attributed to the use of the distributed GWL information to constrain GRACE leakage errors. This study highlights a practical important solution for improving sub‐regional GWSA estimation through the joint use of large‐scale GRACE data and local‐scale well observations. Key Points: A coordinated forward modeling (CoFM) is developed to utilize in situ groundwater level to constrain Gravity Recovery and Climate Experiment leakage errorsCoFM enables the estimation of sub‐regional groundwater storage variations without requiring reliable specific yield dataCoFM estimates groundwater depletion rate in the piedmont unconfined aquifers of North China Plain (NCP) is ∼6 times of that in the east‐central area of NCP [ABSTRACT FROM AUTHOR]

The role of shallow unconfined aquifers in supplying water for evapotranspiration (i.e., groundwater evaporation) is investigated in this paper. Recent results from regional land surface modeling have indicated that in shallow water table areas, a large portion of evapotranspiration comes directly from aquifers. However, little field evidence at the regional scale has been reported to support this finding. Using a comprehensive 19-yr (1984–2002) monthly hydrological dataset on soil moisture, water table depth, and streamflow in Illinois, regional recharge to and evaporation from groundwater are estimated by using soil water balance computation. The 19-yr mean groundwater recharge is estimated to be 244 mm yr−1(25% of precipitation), with uncertainty ranging from 202 to 278 mm yr−1. During the summer, the upward capillary flux from the shallow aquifer helps to maintain a high rate of evapotranspiration. Groundwater evaporation (negative groundwater recharge) occurs during the period of July–September, with a total of 31.4 mm (10% of evapotranspiration). Analysis of the relative soil saturation at 11 depths from 0 to 2 m deep supports the dominance of groundwater evaporation across the water table in dry periods. The zero-flux plane separating the recharge zone from the evapotranspiration zone propagates downward from about 70- to 110-cm depth during summer, reflecting the water supply from progressively lower layers for evapotranspiration. Despite its small magnitude, neglecting regional groundwater evaporation in shallow groundwater areas would result in underestimated root-zone soil moisture and hence evapotranspiration by as large as 20% in the dry summer seasons.

Several recent studies have shown the significance of representing groundwater in land surface hydrologic simulations. However, optimal methods for model parameter calibration in order to realistically simulate baseflow and groundwater depth have received little attention. Most studies still use globally constant groundwater parameters due to the lack of available datasets for calibration. Moreover, when models are calibrated, various parameter combinations are found to exhibit equifinality in simulated total runoff due to model parameter interactions. In this study, a simple lumped groundwater model is incorporated into the Community Land Model (CLM), in which the water table is interactively coupled to soil moisture through the groundwater recharge fluxes. The coupled model (CLMGW) is successfully validated in Illinois using a 22-year (1984-2005) monthly observational dataset. Baseflow estimates from the digital recursive filter technique are used to calibrate the CLMGW parameters. The advantage obtained from incorporating baseflow calibration in addition to traditional calibration based on measured streamflow alone is demonstrated by a Monte Carlo-type simulation analysis. Using the optimal parameter sets identified from baseflow calibration, flow partitioning and water table depth simulations using CLMGW are improved, and the equifinality problem is alleviated. For other regions that lack observations of water table depth, the baseflow calibration approach can be used to enhance parameter estimation and constrain water table depth simulations. © 2008 Elsevier Ltd. All rights reserved.

In this study we estimate the regional terrestrial water storage change (TWSC) and evapotranspiration (ET) in Illinois (∼2 × 105 km2) from reanalysis data for a 22-year period (1984–2005) using terrestrial and atmospheric water balance computations. The estimates are compared with in situ observations of TWSC as well as ET, derived as the residual of observed precipitation, streamflow, and TWSC. The 22-year mean annual cycles of estimated TWSC and ET agree well with observations. Monthly estimates of TWSC and ET match favorably with observations, with correlation coefficients of 0.66 and 0.69, respectively. However, annual average TWSC and ET estimates significantly deviate from the observations; the correlation coefficient drops to 0.31 and 0.19, respectively. A 52-mm/a imbalance (17% of the average streamflow in Illinois) was found between the mean water vapor convergence and streamflow, which also results in a similar magnitude of error in the estimated TWSC and ET based on reanalysis convergence. This imbalance is due in large part to the systematic bias in reanalysis data. It is concluded that the reanalysis data have the potential to estimate the climatology and monthly TWSC and ET variations but are not suitable for the diagnosis of their interannual variability. This is due to the accumulation, rather than cancellation, of the systematic errors in the reanalysis data. Whether the average streamflow would balance the average convergence depends on which particular period of time is under analysis. A longer time period of analysis does not necessarily ensure the balance between streamflow and convergence, as commonly assumed in regional water balance analyses.

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.

Regional groundwater storage changes in Illinois are estimated from monthly GRACE total water storage change (TWSC) data and in situ measurements of soil moisture for the period 2002–2005. Groundwater storage change estimates are compared to those derived from the soil moisture and available well level data. The seasonal pattern and amplitude of GRACE-estimated groundwater storage changes track those of the in situ measurements reasonably well, although substantial differences exist in month-to-month variations. The seasonal cycle of GRACE TWSC agrees well with observations (correlation coefficient = 0.83), while the seasonal cycle of GRACE-based estimates of groundwater storage changes beneath 2 m depth agrees with observations with a correlation coefficient of 0.63. We conclude that the GRACE-based method of estimating monthly to seasonal groundwater storage changes performs reasonably well at the 200,000 km2 scale of Illinois

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.

The Budyko models (BM) have been extended in previous studies by incorporating water storage change (ΔS) (subtracting ΔS from precipitation) to estimate evapotranspiration (ET) under non‐steady state conditions at scales finer than the climatological mean scale. However, a systematic assessment of the interannual ET predictability of the extended BM is still lacking, hence its validity and controlling factors of improvement (over the original BM) under globally diverse climates is not yet well understood. Based on a long‐term (1984–2008) gridded water budget data set, we present a comparative analysis of annual ET predictability between the original BM (ET1) and the extended BM considering ΔS (ET2) in 32 global river basins to explore the sensitivity of climate factors and catchment hydrologic responses in determining ET predictability. Results show that the difference between ET1 and ET2 increases linearly with ΔS, with ET2 < ET1 (ET2 > ET1) when ΔS > 0 (ΔS < 0). When both ET1 and ET2 overestimate (underestimate) observed ET, the error in ET2 is smaller than ET1 when ΔS > 0 (ΔS < 0) for all 32 basins considered. When the error signs of ET2 and ET1 differ, however, the difference in the absolute magnitude of ET2 and ET1 errors (REdiff) under extremely humid climates is determined by the difference between potential ET and ET, leading to comparable accuracy between ET2 and ET1. In contrast, under extremely arid climates, REdiff is controlled by the combined influences of ΔS and R, resulting in more accurate ET2 than ET1 under the condition of the in‐phase, positive‐correlated relationship between ΔS and R. Key Points: The difference in evaporation (ET) estimated from original (ET1) and extended Budyko model (ET2) is linearly with water storage change (ΔS)When both ET1 and ET2 overestimate (underestimate) observed ET, ET2 is more accurate than ET1 if ΔS > 0 (ΔS < 0)If the error signs of ET2 and ET1 differ, their similar accuracy in humid climates is caused by similar ET and potential ET variability [ABSTRACT FROM AUTHOR]