Exploring meso-scale soil water and groundwater storage changes within the USA through a Bayesian combination of GRACE data with monthly 12.5 km model simulations

Climatic changes in precipitation intensity across the United States (USA) may also affect the frequency and magnitude of drought and flooding events, with potentially serious consequences for water supply across this country. Reliable estimation of water storage changes in the soil root zone and groundwater aquifers is important for predicting future water availability, drought and flood monitoring and weather prediction. In this study, we assimilate Terrestrial Water Storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into a water balance model with 12.5-km spatial resolution. Our goal is to explore meso-scale surface and deep-level soil water storage, as well as groundwater changes within the USA covering the period 2003-2017. A new Bayesian approach is formulated and implemented in this study, which provides a dynamic solution for a state-space equation between hydrological model outputs and TWS observations, while considering their error structures. The unknown state parameters and temporal dependency between them are estimated through a combination of forward/backward Kalman Filtering and Markov Chain Monto Carlo (MCMC) methods.The outputs of this methodological approach are evaluated using in situ data from historical USGS groundwater data (over 6600 wells) and the ESA CCI surface soil moisture data. The results indicate that our GRACE data assimilation generally improves the simulation of groundwater and soil moisture across the USA. For example, the long-term linear trend fitted to the Bayesian-derived groundwater and soil water storage are in a same direction as those of in situ data in 63% and 58% of regions studied across the USA, respectively. However, this vale is estimated less than 51% for both water storage estimates derived from the original water balance model, which suggesting that the data assimilation modulates the hydrological models to perform more realistically. The biggest improvements are observed in the southeast USA with considerably large inter-annual variability in precipitation, where modelled groundwater apparently responded too strongly to the changes in atmospheric forcing. The Bayesian data assimilation method also improves the temporal correlation coefficients between the in situ USGS and ESA CCI data and model outputs after merging with GRACE TWS estimates. For instance, the correlation coefficient between groundwater storage and USGS observation increased from -0.52 to 0.48 and from -0.28 to 0.25 in southeast and southwest of USA, respectively. Finally, we will explore changes in Bayesian-derived groundwater and soil water storage within the Florida, California and South of Mississippi regions and interpret their relations with climate-induced factors such as precipitation and ENSO index.Keywords: USA; Data Assimilation; Bayesian Method; Kalman Filtering; MCMC; GRACE; W3RA; groundwater storage; soil water storage; USGS; ESA CCI.&#160;

Download Full-text

A Daily Water Balance Model Based on the Distribution Function Unifying Probability Distributed Model and the SCS Curve Number Method

Water ◽

10.3390/w14020143 ◽

2022 ◽

Vol 14 (2) ◽

pp. 143

Author(s):

Marwan Kheimi ◽

Shokry M. Abdelaziz

Keyword(s):

Distribution Function ◽

Water Balance ◽

Soil Water ◽

Storage Tank ◽

Water Storage ◽

Curve Number ◽

Water Balance Model ◽

Balance Model ◽

Soil Water Storage ◽

Daily Water

A new daily water balance model is developed and tested in this paper. The new model has a similar model structure to the existing probability distributed rainfall runoff models (PDM), such as HyMOD. However, the model utilizes a new distribution function for soil water storage capacity, which leads to the SCS (Soil Conservation Service) curve number (CN) method when the initial soil water storage is set to zero. Therefore, the developed model is a unification of the PDM and CN methods and is called the PDM–CN model in this paper. Besides runoff modeling, the calculation of daily evaporation in the model is also dependent on the distribution function, since the spatial variability of soil water storage affects the catchment-scale evaporation. The generated runoff is partitioned into direct runoff and groundwater recharge, which are then routed through quick and slow storage tanks, respectively. Total discharge is the summation of quick flow from the quick storage tank and base flow from the slow storage tank. The new model with 5 parameters is applied to 92 catchments for simulating daily streamflow and evaporation and compared with AWMB, SACRAMENTO, and SIMHYD models. The performance of the model is slightly better than HyMOD but is not better compared with the 14-parameter model (SACRAMENTO) in the calibration, and does not perform as well in the validation period as the 7-parameter model (SIMHYD) in some areas, based on the NSE values. The linkage between the PDM–CN model and long-term water balance model is also presented, and a two-parameter mean annual water balance equation is derived from the proposed PDM–CN model.

Download Full-text

Analysis of Groundwater and Total Water Storage Changes in Poland Using GRACE Observations, In-situ Data, and Various Assimilation and Climate Models

Remote Sensing ◽

10.3390/rs11242949 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2949 ◽

Cited By ~ 2

Author(s):

Justyna Śliwińska ◽

Monika Birylo ◽

Zofia Rzepecka ◽

Jolanta Nastula

Keyword(s):

Climate Models ◽

Water Storage ◽

Coupled Model ◽

Cmip5 Models ◽

Climate Data ◽

Total Water ◽

In Situ Data ◽

Grace Data ◽

Land Data Assimilation System

The Gravity Recovery and Climate Experiment (GRACE) observations have provided global observations of total water storage (TWS) changes at monthly intervals for over 15 years, which can be useful for estimating changes in GWS after extracting other water storage components. In this study, we analyzed the TWS and groundwater storage (GWS) variations of the main Polish basins, the Vistula and the Odra, using GRACE observations, in-situ data, GLDAS (Global Land Data Assimilation System) hydrological models, and CMIP5 (the World Climate Research Programme’s Coupled Model Intercomparison Project Phase 5) climate data. The research was conducted for the period between September 2006 and October 2015. The TWS data were taken directly from GRACE measurements and also computed from four GLDAS (VIC, CLM, MOSAIC, and NOAH) and six CMIP5 (FGOALS-g2, GFDL-ESM2G, GISS-E2-H, inmcm4, MIROC5, and MPI-ESM-LR) models. The GWS data were obtained by subtracting the model TWS from the GRACE TWS. The resulting GWS values were compared with in-situ well measurements calibrated using porosity coefficients. For each time series, the trends, spectra, amplitudes, and seasonal components were computed and analyzed. The results suggest that in Poland there has been generally no major TWS or GWS depletion. Our results indicate that when comparing TWS values, better compliance with GRACE data was obtained for GLDAS than for CMIP5 models. However, the GWS analysis showed better consistency of climate models with the well results. The results can contribute toward selection of an appropriate model that, in combination with global GRACE observations, would provide information on groundwater changes in regions with limited or inaccurate ground measurements.

Download Full-text

Analysis of groundwater storage changes in main Polish river basins using GRACE observations, in-situ data, and hydrological and climate models

10.5194/egusphere-egu2020-13554 ◽

2020 ◽

Author(s):

Jolanta Nastula ◽

Justyna Śliwińska ◽

Zofia Rzepecka ◽

Monika Birylo

Keyword(s):

Climate Models ◽

Water Storage ◽

Coupled Model ◽

Research Programme ◽

Climate Data ◽

Groundwater Storage ◽

In Situ Data ◽

Snow Water ◽

Land Data Assimilation System

The Gravity Recovery and Climate Experiment (GRACE) measurements have provided global observations of total water storage (TWS) changes at monthly intervals for almost 20 years. They are useful for estimating changes in groundwater storage (GWS) after extracting other water storage components like soil water or snow water.In this study, we analyse the GWS variations of two main Polish basins, the Vistula and the Odra, using GRACE observations, in-situ wells measurements, GLDAS (Global Land Data Assimilation System) hydrological models, and CMIP5 (the World Climate Research Programme&#8217;s Coupled Model Intercomparison Project Phase 5) climate data. The research is conducted for the period between September 2006 and October 2015.Here, TWS is taken directly from GRACE measurements and also computed from all considered models. GWS is obtained by subtracting the modelled sum of soil moisture and snow water from the GRACE-based TWS. The resultant GWS series are validated by comparing with appropriately calibrated in-situ wells measurements. For each GWS time series, the trends, spectra, amplitudes, and seasonal components were computed and analysed. The results suggest that in Poland there has been generally no major GWS depletion. The results can contribute toward selection of an appropriate model that, in combination with GRACE observations, would provide information on groundwater changes in regions with limited or inaccurate in-situ groundwater storage measurements.

Download Full-text

Diagnosis toward predicting mean annual runoff in ungauged basins

Hydrology and Earth System Sciences ◽

10.5194/hess-25-945-2021 ◽

2021 ◽

Vol 25 (2) ◽

pp. 945-956

Author(s):

Yuan Gao ◽

Lili Yao ◽

Ni-Bin Chang ◽

Dingbao Wang

Keyword(s):

Water Balance ◽

Soil Water ◽

Storage Capacity ◽

Water Storage ◽

Annual Runoff ◽

Balance Model ◽

Soil Water Storage ◽

Ungauged Basins ◽

Water Storage Capacity ◽

Soil Water Storage Capacity

Abstract. Prediction of mean annual runoff is of great interest but still poses a challenge in ungauged basins. The present work diagnoses the prediction in mean annual runoff affected by the uncertainty in estimated distribution of soil water storage capacity. Based on a distribution function, a water balance model for estimating mean annual runoff is developed, in which the effects of climate variability and the distribution of soil water storage capacity are explicitly represented. As such, the two parameters in the model have explicit physical meanings, and relationships between the parameters and controlling factors on mean annual runoff are established. The estimated parameters from the existing data of watershed characteristics are applied to 35 watersheds. The results showed that the model could capture 88.2 % of the actual mean annual runoff on average across the study watersheds, indicating that the proposed new water balance model is promising for estimating mean annual runoff in ungauged watersheds. The underestimation of mean annual runoff is mainly caused by the underestimation of the area percentage of low soil water storage capacity due to neglecting the effect of land surface and bedrock topography. Higher spatial variability of soil water storage capacity estimated through the height above the nearest drainage (HAND) and topographic wetness index (TWI) indicated that topography plays a crucial role in determining the actual soil water storage capacity. The performance of mean annual runoff prediction in ungauged basins can be improved by employing better estimation of soil water storage capacity including the effects of soil, topography, and bedrock. It leads to better diagnosis of the data requirement for predicting mean annual runoff in ungauged basins based on a newly developed process-based model finally.

Download Full-text

Exploring groundwater and soil water storage changes across the CONUS at 12.5 km resolution by a Bayesian integration of GRACE data into W3RA

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.143579 ◽

2021 ◽

Vol 758 ◽

pp. 143579

Author(s):

Nooshin Mehrnegar ◽

Owen Jones ◽

Michael Bliss Singer ◽

Maike Schumacher ◽

Thomas Jagdhuber ◽

...

Keyword(s):

Soil Water ◽

Water Storage ◽

Soil Water Storage ◽

Bayesian Integration ◽

Grace Data

Download Full-text

Diagnosis toward predicting mean annual runoff in ungauged basins

10.5194/hess-2020-353 ◽

2020 ◽

Author(s):

Yuan Gao ◽

Lili Yao ◽

Ni-Bin Chang ◽

Dingbao Wang

Keyword(s):

Soil Water ◽

Storage Capacity ◽

Water Storage ◽

Annual Runoff ◽

Balance Model ◽

Soil Water Storage ◽

Ungauged Basins ◽

Water Storage Capacity ◽

Soil Water Storage Capacity ◽

Soil Storage Capacity

Abstract. The present work diagnoses the prediction in mean annual runoff affected by the uncertainty in estimated distribution of soil water storage capacity. Based on a distribution function, a water balance model for estimating mean annual runoff is developed, in which the effects of climate variability and the distribution of soil water storage capacity are explicitly represented. As such, the two parameters in the model have explicit physical meanings, and relationships between the parameters and controlling factors on mean annual runoff are established. The estimated parameters from the existing data of watershed characteristics are applied to 35 watersheds. The results showed that the model could capture 88.2 % of the actual runoff on average, indicating that the proposed new water balance model is promising for estimating mean annual runoff in ungauged watersheds. The underestimation of runoff is mainly caused by the underestimation of the spatial heterogeneity of soil storage capacity due to neglecting the effect of land surface and bedrock topography. A higher spatial variability of soil storage capacity estimated through the Height Above the Nearest Drainage (HAND) indicated that topography plays a crucial role in determining the actual soil water storage capacity. The performance of mean annual runoff prediction in ungauged basins can be improved by employing better estimation of soil water storage capacity including the effects of soil, topography and bedrock. The purpose of this study is to diagnose the data requirement for predicting mean annual runoff in ungauged basins based on a newly developed process-based model.

Download Full-text

Improved Understanding of Groundwater Storage Changes under the Influence of River Basin Governance in Northwestern China Using GRACE Data

Remote Sensing ◽

10.3390/rs13142672 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2672

Author(s):

Xin Liu ◽

Litang Hu ◽

Kangning Sun ◽

Zhengqiu Yang ◽

Jianchong Sun ◽

...

Keyword(s):

River Basin ◽

Groundwater Level ◽

Regional Scale ◽

Water Storage ◽

Northwestern China ◽

In Situ Observation ◽

Observation Data ◽

Groundwater Storage ◽

Grace Data

Groundwater is crucial for economic development in arid and semiarid areas. The Shiyang River Basin (SRB) has the most prominent water use issues in northwestern China, and overexploited groundwater resources have led to continuous groundwater-level decline. The key governance planning project of the SRB was issued in 2007. This paper synthetically combines remote-sensing data from Gravity Recovery and Climate Experiment (GRACE) data and precipitation, actual evapotranspiration, land use, and in situ groundwater-level data to evaluate groundwater storage variations on a regional scale. Terrestrial water storage anomalies (TWSA) and groundwater storage anomalies (GWSA), in addition to their influencing factors in the SRB since the implementation of the key governance project, are analyzed in order to evaluate the effect of governance. The results show that GRACE-derived GWS variations are consistent with in situ observation data in the basin, with a correlation coefficient of 0.68. The GWS in the SRB had a slow downward trend from 2003 to 2016, and this increased by 0.38 billion m³/year after 2018. As the meteorological data did not change significantly, the changes in water storage are mainly caused by human activities, which are estimated by using the principle of water balance. The decline in GWS in the middle and lower reaches of the SRB has been curbed since 2009 and has gradually rebounded since 2014. GWS decreased by 2.2 mm EWH (equivalent water height) from 2011 to 2016, which was 91% lower than that from 2007 to 2010. The cropland area in the middle and lower reaches of the SRB also stopped increasing after 2011 and gradually decreased after 2014, while the area of natural vegetation gradually increased, indicating that the groundwater level and associated ecology significantly recovered after the implementation of the project.

Download Full-text

Comparison of real evapotranspiration measured by weighing lysimeters with simulations based on the Penman formula and a crop growth model

Journal of Hydrology and Hydromechanics ◽

10.2478/johh-2013-0021 ◽

2013 ◽

Vol 61 (2) ◽

pp. 161-172 ◽

Cited By ~ 10

Author(s):

Martin Wegehenkel ◽

Horst H. Gerke

Keyword(s):

Water Balance ◽

Soil Water ◽

Growth Model ◽

Temporal Dynamics ◽

Water Storage ◽

Water Balance Model ◽

Soil Water Balance ◽

Balance Model ◽

Soil Water Storage ◽

Silty Clay

Abstract Although the quantification of real evapotranspiration (ETr) is a prerequisite for an appropriate estimation of the water balance, precision and uncertainty of such a quantification are often unknown. In our study, we tested a combined growth and soil water balance model for analysing the temporal dynamics of ETr. Simulated ETr, soil water storage and drainage rates were compared with those measured by 8 grass-covered weighable lysimeters for a 3-year period (January 1, 1996 to December 31, 1998). For the simulations, a soil water balance model based on the Darcy-equation and a physiological-based growth model for grass cover for the calculation of root water uptake were used. Four lysimeters represented undisturbed sandy soil monoliths and the other four were undisturbed silty-clay soil monoliths. The simulated ETr-rates underestimated the higher ETr-rates observed in the summer periods. For some periods in early and late summer, the results were indicative for oasis effects with lysimeter-measured ETr-rates higher than corresponding calculated rates of potential grass reference evapotranspiration. Despite discrepancies between simulated and observed lysimeter drainage, the simulation quality for ETr and soil water storage was sufficient in terms of the Nash-Sutcliffe index, the modelling efficiency index, and the root mean squared error. The use of a physiological-based growth model improved the ETr estimations significantly.

Download Full-text

Water storage change estimation from in situ shrinkage measurements of clay soils

Hydrology and Earth System Sciences ◽

10.5194/hess-17-1933-2013 ◽

2013 ◽

Vol 17 (5) ◽

pp. 1933-1949 ◽

Cited By ~ 20

Author(s):

B. te Brake ◽

M. J. van der Ploeg ◽

G. H. de Rooij

Keyword(s):

Soil Water ◽

Volume Change ◽

Water Loss ◽

Water Storage ◽

Bulk Soil ◽

Soil Water Storage ◽

Dry Period ◽

Soil Layers ◽

Storage Change

Abstract. The objective of this study is to assess the applicability of clay soil elevation change measurements to estimate soil water storage changes, using a simplified approach. We measured moisture contents in aggregates by EC-5 sensors, and in multiple aggregate and inter-aggregate spaces (bulk soil) by CS616 sensors. In a long dry period, the assumption of constant isotropic shrinkage proved invalid and a soil moisture dependant geometry factor was applied. The relative overestimation made by assuming constant isotropic shrinkage in the linear (basic) shrinkage phase was 26.4% (17.5 mm) for the actively shrinking layer between 0 and 60 cm. Aggregate-scale water storage and volume change revealed a linear relation for layers ≥ 30 cm depth. The range of basic shrinkage in the bulk soil was limited by delayed drying of deep soil layers, and maximum water loss in the structural shrinkage phase was 40% of total water loss in the 0–60 cm layer, and over 60% in deeper layers. In the dry period, fitted slopes of the ΔV–ΔW relationship ranged from 0.41 to 0.56 (EC-5) and 0.42 to 0.55 (CS616). Under a dynamic drying and wetting regime, slopes ranged from 0.21 to 0.38 (EC-5) and 0.22 to 0.36 (CS616). Alternating shrinkage and incomplete swelling resulted in limited volume change relative to water storage change. The slope of the ΔV–ΔW relationship depended on the drying regime, measurement scale and combined effect of different soil layers. Therefore, solely relying on surface level elevation changes to infer soil water storage changes will lead to large underestimations. Recent and future developments might provide a basis for application of shrinkage relations to field situations, but in situ observations will be required to do so.

Download Full-text

Groundwater storage change in the Jinsha River basin from GRACE, hydrologic models, and in situ data

10.5194/egusphere-egu2020-1281 ◽

2020 ◽

Author(s):

Nengfang Chao

Keyword(s):

Sediment Transport ◽

River Basin ◽

Human Activities ◽

Water Storage ◽

Hydrologic Models ◽

Groundwater Storage ◽

Jinsha River ◽

In Situ Data ◽

Storage Change

Groundwater plays a major role in the hydrological processes driven by climate change and human activities, particularly in upper mountainous basins. The Jinsha River Basin (JRB) is the uppermost region of the Yangtze River and the largest hydropower production region in China. With the construction of artificial cascade reservoirs increasing in this region, the annual and seasonal flows are changing and affecting the water cycles. Here, we first infer the groundwater storage changes (GWSC), accounting for sediment transport in JRB, by combining the Gravity Recovery and Climate Experiment (GRACE) mission, hydrologic models and in situ data. The results indicate: (1) the average estimation of the GWSC trend, accounting for sediment transport in JRB, is 0.76&#177;0.10 cm/year during the period 2003&#8211;2015, and the contribution of sediment transport accounts for 15%; (2) precipitation (P), evapotranspiration (ET), soil moisture change (SMC), GWSC and land water storage changes (LWSC) show clear seasonal cycles; the interannual trends of LWSC and GWSC increase, but P, runoff (R), surface water storage change (SWSC) and SMC decrease, and ET remains basically unchanged; (3) the main contributor to the increase in LWSC in JRB is GWSC, and the increased GWSC may be dominated by human activities, such as cascade damming, and climate variations (such as snow and glacier melt due to increased temperatures). This study can provide valuable information regarding JRB in China for understanding GWSC patterns and exploring their implications for regional water management.

Download Full-text