Terrestrial Water Storage Estimates Research Articles

Sensitivity of GNSS‐Derived Estimates of Terrestrial Water Storage to Assumed Earth Structure

AbstractGeodetic methods can monitor changes in terrestrial water storage (TWS) across large regions in near real‐time. Here, we investigate the effect of assumed Earth structure on TWS estimates derived from Global Navigation Satellite System (GNSS) displacement time series. Through a series of synthetic tests, we systematically explore how the spatial wavelength of water load affects the error of TWS estimates. Large loads (e.g., >1,000 km) are well recovered regardless of the assumed Earth model. For small loads (e.g., <10 km), however, errors can exceed 75% when an incorrect model for the Earth is chosen. As a case study, we consider the sensitivity of seasonal TWS estimates within mountainous watersheds of the western U.S., finding estimates that differ by over 13% for a collection of common global and regional structural models. Errors in the recovered water load generally scale with the total weight of the load; thus, long‐term changes in storage can produce significant uplift (subsidence), enhancing errors. We demonstrate that regions experiencing systematic and large‐scale variations in water storage, such as the Greenland ice sheet, exhibit significant differences in predicted displacement (over 20 mm) depending on the choice of Earth model. Since the discrepancies exceed GNSS observational precision, an appropriate Earth model must be adopted when inverting GNSS observations for mass changes in these regions. Furthermore, regions with large‐scale mass changes that can be quantified using independent data (e.g., altimetry, gravity) present opportunities to use geodetic observations to refine structural properties of seismologically derived models for the Earth's interior structure.

GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present

Abstract. Terrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional understanding of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940–2022) and relatively high-resolution (i.e., 0.25∘) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine-learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent products such as the land–ocean mass budget, atmospheric and terrestrial water budget in 341 large river basins, and streamflow measurements at 10 168 gauges. The results show that our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets. Moreover, our reconstructions successfully reproduce the consequences of climate variability such as strong El Niño events. The GTWS-MLrec dataset consists of three reconstructions based on (a) mascons of the Jet Propulsion Laboratory of the California Institute of Technology, the Center for Space Research at the University of Texas at Austin, and the Goddard Space Flight Center of NASA; (b) three detrended and de-seasonalized reconstructions; and (c) six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a wide range of geoscience applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management. GTWS-MLrec is available on Zenodo through https://doi.org/10.5281/zenodo.10040927 (Yin, 2023).

MONITORING GROUNDWATER STORAGE BASINS AND HYDROLOGICAL CHANGES USING THE GRACE SATELLITE AND SENTINEL-1 FOR THE GANGA RIVER BASIN

Abstract. Groundwater depletion-related subsidence is a significant issue in many parts of the world. It can permanently reduce the amount of groundwater stored in an aquifer and even cause structural damage to the Earth’s surface. The Ganga Basin in the northwestern region of India is no exception, with around a meter of subsidence occurring between 2018 and 2023. However, understanding the connection between variations in groundwater quantities and ground deformation has been challenging. We used surface displacement measurements from InSAR and gravimetric terrestrial water storage estimates from the GRACE satellite pair to characterize the hydrological dynamics within the Ganga Basin. Sentinel-1 was used to map the entire Ganga River basin in the inundated zone. The InSAR time series shows coherent short-term changes that coincide with hydrological features when the long-term aquifer compaction is removed. For instance, an uplift is seen at the confluence of multiple rivers and streams that drain into the southeastern margin of the basin in the winters of 2018–2019 and 2021–2022. Imaging the monthly spatial variations in water volumes is based on these data and calculations of mass changes from the orbiting of Sentinel-1 and GRACE satellites. We even employ machine learning techniques as evaluative methods to make it simple to combine InSAR quickly and convincingly with gravimetric datasets, which will help advance global efforts to understand better and manage groundwater resources.

Simulation and Driving Factor Analysis of Satellite-Observed Terrestrial Water Storage Anomaly in the Pearl River Basin Using Deep Learning

Accurate estimation of terrestrial water storage (TWS) and understanding its driving factors are crucial for effective hydrological assessment and water resource management. The launches of the Gravity Recovery and Climate Experiment (GRACE) satellites and their successor, GRACE Follow-On (GRACE-FO), combined with deep learning algorithms, have opened new avenues for such investigations. In this study, we employed a long short-term memory (LSTM) neural network model to simulate TWS anomaly (TWSA) in the Pearl River Basin (PRB) from 2003 to 2020, using precipitation, temperature, runoff, evapotranspiration, and leaf area index (LAI) data. The performance of the LSTM model was rigorously evaluated, achieving a high average correlation coefficient (r) of 0.967 and an average Nash–Sutcliffe efficiency (NSE) coefficient of 0.912 on the testing set. To unravel the relative importance of each driving factor and assess the impact of different lead times, we employed the SHapley Additive exPlanations (SHAP) method. Our results revealed that precipitation exerted the most significant influence on TWSA in the PRB, with a one-month lead time exhibiting the greatest impact. Evapotranspiration, runoff, temperature, and LAI also played important roles, with interactive effects among these factors. Moreover, we observed an accumulation effect of precipitation and evapotranspiration on TWSA, particularly with shorter lead times. Overall, the SHAP method provides an alternative approach for the quantitative analysis of natural driving factors at the basin scale, shedding light on the natural dominant influences on TWSA in the PRB. The combination of satellite observations and deep learning techniques holds promise for advancing our understanding of TWS dynamics and enhancing water resource management strategies.

Comparative Analysis of Global Terrestrial Water Storage Simulations: Assessing CABLE, Noah-MP, PCR-GLOBWB, and GLDAS Performances during the GRACE and GRACE-FO Era

Hydrology and land surface and models (HM and LSM) are essential tools for estimating global terrestrial water storage (TWS), an important component of the global water budget for assessing the accessibility and long-term variability of water supplies. With the expansion of open-source and open-data policies, the community can now perform model TWS simulation from source codes as well as directly exploit end-user hydrologic products for water resource applications. Regardless of the model effectiveness and usability, an accuracy assessment is necessary to quantify the model’s global and regional strengths, weaknesses, and reliability. This paper compares the most recent global TWS estimates from six models, namely the PCRaster Global Water Balance (PCR-GLOBWB), Noah, Noah-Multiparameterization (Noah-MP), Catchment LSM, and Variable Infiltration Capacity (VIC), and Community Atmosphere Biosphere Land Exchange (CABLE)—the latter of which is cross validated for the first time. TWS observations from the Gravity Recovery And Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite missions between 2002 and 2021 are used to validate the model. The analyses show that Noah-MP outperforms other models in terms of global average correlations and root mean square errors. PCR-GLOBWB performance is superior in irrigated regions because of the inclusion of human intervention components in the model. CABLE, a core LSM of the Australian climate model, significantly outperforms all others in Australia. CLSM performs reasonably well, but the TWS long-term trend appears to be incorrect due to an overestimated groundwater component. Noah performs similarly (but inferiorly) to Noah-MP, most likely due to model physics sharing. VIC has the least agreement with GRACE and GRACE-FO. The evaluation also sheds some light on the role of forcing data in model performance, particularly for ready-to-use products such as GLDAS, where incorporating MERRA-2 or ERA5 data into GLDAS Noah simulations may potentially improve its TWS accuracy, which has previously been overlooked due to limited modeling capacity. Despite each model’s unique strength, the ensemble mean TWS, particularly when Noah-MP and PCR-GLOBWB are included, yields better TWS estimates than an individual model result. The findings of this study could serve as a benchmark for future model development and the data published in this paper could aid in the scientific advancement and discoveries of the hydrology community.

Gridded maps of wetlands dynamics over mid-low latitudes for 1980–2020 based on TOPMODEL

Dynamics of global wetlands are closely linked to biodiversity conservation, hydrology, and greenhouse gas emissions. However, long-term time series of global wetland products are still lacking. Using a diagnostic model based on the TOPography-based hydrological MODEL (TOPMODEL), this study produced an ensemble of 28 gridded maps of monthly global/regional wetland extents (with more reliable estimates at mid-low latitudes) for 1980–2020 at 0.25° × 0.25° spatial resolution, calibrated with a combination of four observation-based wetland data and seven gridded soil moisture reanalysis datasets. The gridded dynamic maps of wetlands capture the spatial distributions, seasonal cycles, and interannual variabilities of observed wetland extent well, and also show a good agreement with independent satellite-based terrestrial water storage estimates over wetland areas. The long temporal coverage extending beyond the era of satellite datasets, the global coverage, and the opportunity to provide real-time updates from ongoing soil moisture data make these products helpful for various applications such as analyzing the wetland-related methane emission.

Assimilation of SBAS-InSAR Based Vertical Deformation Into Land Surface Model to Improve the Estimation of Terrestrial Water Storage

The gravity recovery and climate experiment (GRACE) provides an unprecedented opportunity to detect the spatial and temporal variation of the terrestrial water storage (TWS) for regional to continental scales. However, the GRACE system's coarse temporal resolution (∼monthly) and data discontinuity missing perplexed the TWS research during the operation. In this article, the data assimilation (DA) method was employed to integrate the vertical deformation obtained from the small baseline subset (SBAS) InSAR processing into the NASA catchment land surface model (CLSM), which improved the estimation of the TWS. First, we used a one-dimensional ensemble Kalman filter for DA research to estimate the TWS in Dali Prefecture, southwestern China. Finally, we compared the estimated TWS with the GRACE-based TWS from December 2, 2018 to January 21, 2021. The unbiased root-mean-square of the open loop (OL; without DA) method and the SBAS-InSAR DA method are 61 mm and 30 mm in Dali Prefecture, respectively. Results revealed that the numerical difference between the estimated TWS and the GRACE TWS retrievals was significantly decreased by the SBAS-InSAR DA method than the OL method. In addition, the temporal resolution of the SBAS-InSAR DA-based TWS was improved to 12 days compared with GRACE-based TWS. Furthermore, we recovered the discontinuous deletion and blank of GRACE-based TWS from 2015 to 2018 by the SBAS-InSAR DA method.

Multi-type assessment of global droughts and teleconnections

Several drought indices have been developed based on various processes (e.g., precipitation, soil moisture, vegetation health) that respond differently to modes of climate variability, shadowing their relatability to teleconnections, which in turn, limits drought forecasting. In this study, we advanced the multivariate analysis of droughts by using long-term Terrestrial Water Storage estimates, soil moisture and precipitation data along with normalized difference vegetation index. To this end, we employed a Vine copula approach using Archimedean and Elliptical copula families to generate two novel multivariate drought indices called Combined Standardized Drought Index (CSDI), based on agricultural, meteorological, hydrological and ecological univariate indices (i.e., the Eco-meteo-hydrologic index and the Agro-meteo-hydrologic index) for 33 major river basins across the globe between 1982 and 2015. To overcome the challenges associated with vine copula building blocks, we exhausted the possible choices of vine trees and selected the superior model based on a variety of performance metrics. CSDIs showed an integrated representation of univariate drought indices and revealed a more comprehensive and improved picture of intensity, duration and frequency of droughts. Our composite analysis showed that El Niño and La Niña have a significant impact on the regional drought occurrences across the globe, with highest impacts observed for fall. Results also showed that CSDIs can extract more conclusive anomalies in response to ENSO signals than univariate indices, as they better represent the ecosystem response to teleconnections.

Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin

Terrestrial water storage (TWS) is a critical variable in the global hydrological cycle. The TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) allow us to better understand water exchanges between the atmosphere, land surface, sea, and glaciers. However, missing historical (pre-2002) GRACE data limit their further application. In this study, we developed a random forest (RF) model to reconstruct the monthly terrestrial water storage anomaly (TWSA) time series using Global Land Data Assimilation System (GLDAS) and Climatic Research Unit (CRU) data for the Lancang-Mekong River basin. The results show that the RF-built TWSA time series agrees well with the GRACE TWSA time series for 2003–2014, showing that correlation coefficients (R) of 0.97 and 0.90 at the basin and grid scales, respectively, which demonstrates the reliability of the RF model. Furthermore, this method is used to reconstruct the historical TWSA time series for 1980–2002. Moreover, the discharge can be obtained by subtracting the evapotranspiration (ET) and RF-built terrestrial water storage change (TWSC) from the precipitation. The comparison between the discharge calculated from the water balance method and the observed discharge showed significant consistency, with a correlation coefficient of 0.89 for 2003–2014 but a slightly lower correlation coefficient (0.86) for 1980–2002. The methods and findings in this study can provide an effective means of reconstructing the TWSA and discharge time series in basins with sparse hydrological data.

Development and evaluation of 0.05° terrestrial water storage estimates using Community Atmosphere Biosphere Land Exchange (CABLE) land surface model and assimilation of GRACE data

Abstract. Accurate estimation of terrestrial water storage (TWS) at a high spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5∘ by the resolution of soil and vegetation data sets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5 to 0.05∘) and time span (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05∘ developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. Using improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated on in this paper relies only on publicly accessible global data sets, allowing the reproduction of the 0.05∘ TWS estimates in any study region.

Multiple Data Products Reveal Long-Term Variation Characteristics of Terrestrial Water Storage and Its Dominant Factors in Data-Scarce Alpine Regions

As the “Water Tower of Asia” and “The Third Pole” of the world, the Qinghai–Tibet Plateau (QTP) shows great sensitivity to global climate change, and the change in its terrestrial water storage has become a focus of attention globally. Differences in multi-source data and different calculation methods have caused great uncertainty in the accurate estimation of terrestrial water storage. In this study, the Yarlung Zangbo River Basin (YZRB), located in the southeast of the QTP, was selected as the study area, with the aim of investigating the spatio-temporal variation characteristics of terrestrial water storage change (TWSC). Gravity Recovery and Climate Experiment (GRACE) data from 2003 to 2017, combined with the fifth-generation reanalysis product of the European Centre for Medium-Range Weather Forecasts (ERA5) data and Global Land Data Assimilation System (GLDAS) data, were adopted for the performance evaluation of TWSC estimation. Based on ERA5 and GLDAS, the terrestrial water balance method (PER) and the summation method (SS) were used to estimate terrestrial water storage, obtaining four sets of TWSC, which were compared with TWSC derived from GRACE. The results show that the TWSC estimated by the SS method based on GLDAS is most consistent with the results of GRACE. The time-lag effect was identified in the TWSC estimated by the PER method based on ERA5 and GLDAS, respectively, with 2-month and 3-month lags. Therefore, based on the GLDAS, the SS method was used to further explore the long-term temporal and spatial evolution of TWSC in the YZRB. During the period of 1948–2017, TWSC showed a significantly increasing trend; however, an abrupt change in TWSC was detected around 2002. That is, TWSC showed a significantly increasing trend before 2002 (slope = 0.0236 mm/month, p < 0.01) but a significantly decreasing trend (slope = −0.397 mm/month, p < 0.01) after 2002. Additional attribution analysis on the abrupt change in TWSC before and after 2002 was conducted, indicating that, compared with the snow water equivalent, the soil moisture dominated the long-term variation of TWSC. In terms of spatial distribution, TWSC showed a large spatial heterogeneity, mainly in the middle reaches with a high intensity of human activities and the Parlung Zangbo River Basin, distributed with great glaciers. The results obtained in this study can provide reliable data support and technical means for exploring the spatio-temporal evolution mechanism of terrestrial water storage in data-scarce alpine regions.

Monitoring Terrestrial Water Storage Changes with the Tongji-Grace2018 Model in the Nine Major River Basins of the Chinese Mainland

Data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission can be used to monitor changes in terrestrial water storage (TWS). In this study, we exploit the TWS observations from a new temporal gravity field model, Tongji-Grace2018, which was developed using an optimized short-arc approach at Tongji University. We analyzed the changes in the TWS and groundwater storage (GWS) in each of the nine major river basins of the Chinese mainland from April 2002 to August 2016, using Tongji-Grace2018, the Global Land Data Assimilation System (GLDAS) hydrological model, in situ observations, and additional auxiliary data (such as precipitation and temperature). Our results indicate that the TWS of the Songliao, Yangtze, Pearl, and Southeastern River Basins are all increasing, with the most drastic TWS growth occurring in the Southeastern River Basin. The TWS of the Yellow, Haihe, Huaihe, and Southwestern River Basins are all decreasing, with the most drastic TWS loss occurring in the Haihe River Basin. The Continental River Basin TWS has remained largely unchanged over time. With the exception of the Songliao and Pearl River Basins, the GWS results produced by the Tongji-Grace2018 model are consistent with the in situ observations of these basins. The correlation coefficients for the Tongji-Grace2018 model results and the in situ observations for the Yellow, Huaihe, Yangtze, Southwestern, and Continental River Basins are higher than 0.710. Overall, the GWS results for the Songliao, Yellow, Haihe, Huaihe, Southwestern, and Continental River Basins all exhibit a downward trend, with the most severe groundwater loss occurring in the Haihe and Huaihe River Basins. However, the Yangtze and Southeastern River Basins both have upward-trending modeled and measured GWS values. This study demonstrates the effectiveness of the Tongji-Grace2018 model for the reliable estimation of TWS and GWS changes on the Chinese mainland, and may contribute to the management of available water resources.

Global Soil Water Estimates as Landslide Predictor: The Effectiveness of SMOS, SMAP, and GRACE Observations, Land Surface Simulations, and Data Assimilation

AbstractThis global feasibility study assesses the potential of coarse-scale, gridded soil water estimates for the probabilistic modeling of hydrologically triggered landslides, using Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery and Climate Experiment (GRACE) remote sensing data; Catchment Land Surface Model (CLSM) simulations; and six data products based on the assimilation of SMOS, SMAP, and/or GRACE observations into CLSM. SMOS or SMAP observations (~40-km resolution) are only available for less than 20% of the globally reported landslide events, because they are intermittent and uncertain in regions with complex terrain. GRACE terrestrial water storage estimates include 75% of the reported landslides but have coarse spatial and temporal resolutions (monthly, ~300 km). CLSM soil water simulations have the added advantage of complete spatial and temporal coverage, and are found to be able to distinguish between “stable slope” (no landslide) conditions and landslide-inducing conditions in a probabilistic way. Assimilating SMOS and/or GRACE data increases the landslide probability estimates based on soil water percentiles for the reported landslides, relative to model-only estimates at 36-km resolution for the period 2011–16, unless the CLSM model-only soil water content is already high (≥50th percentile). The SMAP Level 4 data assimilation product (at 9-km resolution, period 2015–19) more generally updates the soil water conditions toward higher landslide probabilities for the reported landslides, but is similar to model-only estimates for the majority of landslides where SMAP data cannot easily be converted to soil moisture owing to complex terrain.

Assimilation of Ground‐Based GPS Observations of Vertical Displacement into a Land Surface Model to Improve Terrestrial Water Storage Estimates

AbstractGround‐based Global Positioning System (GPS) observations of vertical surface displacement can be used to study terrestrial water storage (TWS) change after properly accounting for nonhydrological loading effects. This study systematically merged ground‐based GPS observations of vertical displacement into a land surface model in order to better estimate TWS. Assimilation was conducted across two snow‐dominated watersheds in the western United States using a one‐dimensional ensemble Kalman filter (EnkF). Modeled estimates were compared against TWS retrievals derived from the Gravity Recovery and Climate Experiment (GRACE) mission and in situ measurements of snow water equivalent (SWE), soil moisture, and runoff. The GPS data assimilation (GPS DA) technique improves prediction skill of TWS anomalies relative to the Open Loop (OL; model run without assimilation) when compared against GRACE TWS retrievals, especially during an extended drought period post‐2011 (e.g., correlation coefficient ROL = 0.46 and RGPSDA = 0.82 in the Great Basin). Furthermore, GPS DA improves SWE estimation with improved R values found over 76% and 69% of all pixels collocated with in situ stations in the Great Basin and Upper Colorado watersheds, respectively. GPS DA estimates of surface soil moisture and runoff, however, exhibit degraded performance relative to the OL due to the limited sensitivity of GPS observations to surface soil moisture variations and the lack of a dynamic surface routing scheme as well as other missing model physics (e.g., river and dam regulation, irrigation‐related water withdrawals, surface water impoundments) that are not included in the Catchment Land Surface Model.

The potential of GRACE in assessing the flood potential of Peninsular Indian River basins

ABSTRACT The existence of fewer gauging sites along with sparse network of rain gauges make the monitoring of floods difficult, especially over regions characterized with hydrologic heterogeneity. The availability of observations from satellite sensors makes it possible to study hydrological behaviour of such under-observed river basins. In this study, we explore the utility of GRACE derived terrestrial water storage estimates to monitor the flood events of varying magnitudes over the river basins on Indian Peninsula, that is known to be a region of high heterogeneity. We use terrestrial water storage estimates from Gravity Recovery and Climate Experiment (GRACE) satellite mission, rainfall and temperature data from India Meteorological Department (IMD), discharge data from Central Water Commission (CWC) and groundwater level data from Central Ground Water Board (CGWB) to evaluate and compare the variability of flood potential of six major river basins, which show unique hydrological characteristics. The flood potential is evaluated for these major basins in Peninsular India using Reager’s Flood Potential Index (FPI) and the skill of FPI is assessed over these basins, for the period from 2003 to 2016. Significant variations are observed in the terrestrial water storage and FPI estimates of these basins, which reflects the diverse hydrological behaviour of the basins. Mahanadi basin shows extremely high positive FPI values, while Cauvery basin shows extremely high negative values, thereby confirming their respective flood and drought prone characteristics. The ability of FPI is established especially for basins with discharge values higher than 2000 m3 s–1. Moreover, good accuracy (> 90%) is observed over Mahanadi, Godavari and Narmada basins that are exposed with frequent mild, moderate and severe floods, using the threshold values of FPI. Among all the basins, Mahanadi basin shows high threshold FPI values of 0.40, 0.32 and 0.27 for severe, moderate and mild floods respectively. Godavari and Narmada basins have relatively lesser thresholds. The FPI values corresponding to severe, moderate and mild floods for Godavari basin are 0.25, 0.14 and 0.09, while those for Narmada basin are 0.25, 0.11 and 0.04. We hence, conclude that the index has the ability to monitor different levels of floods, over basins exhibiting diverse hydrological characteristics. These observations establish that GRACE derived terrestrial water storage anomaly (TWSA) can be used for flood monitoring applications, when discharge data is rarely available and the incorporation of terrestrial water storage estimates with the rainfall data in hydrological modelling may greatly help in assessing hydrological extremes.

Comparison of Vertical Surface Deformation Estimates Derived From Space‐Based Gravimetry, Ground‐Based GPS, and Model‐Based Hydrologic Loading Over Snow‐Dominated Watersheds in the United States

AbstractSpatiotemporal variability in Earth's terrestrial water storage (TWS) causes changes in surface deformation. The potential for using ground‐based Global Positioning System (GPS) vertical displacement observations for estimating TWS is explored through a comparison of vertical displacements derived from space‐based gravimetric retrievals, ground‐based GPS, and model‐based hydrologic estimates. The study presented here focuses on two snow‐dominated basins in the Western United States for the years 2003–2016. Seasonal variations are observed in the vertical displacements derived from all three data sets, and the variation is coherent with the changes in hydrologic loading. Good consistency is observed between any two of the three data sets with gravimetric retrievals and hydrologic model estimates providing the highest level of agreement (i.e., all examined stations with correlation coefficient R > 0.70). Vertical displacements derived from gravimetric retrievals and ground‐based GPS yielded R > 0.70 for more than 89% of the stations. In addition, it is found that both GPS‐derived and space‐based, gravimetry‐derived vertical displacements clearly reflected the impact of climate variation (i.e., heavy precipitation during 2010–2011 winter followed by prolonged drought). Vertical displacements derived from the hydrologic model highlighted the relatively large precipitation convergence phase during late 2010 to early 2011 at some stations but not the prolonged drought that followed. The results indicate that ground‐based GPS observations of vertical displacement have the capability to capture variations in TWS changes, which can be systematically merged in conjunction with Gravity Recovery and Climate Experiment (GRACE) into a land surface model to improve TWS estimates in a follow‐up study.

Potential future changes of terrestrial water storage based on climate projections by ensemble model simulations

An accurate estimation of terrestrial water storage (TWS) is crucial for water resource management and drought monitoring. However, the uncertainties in model physics, surface parameters and meteorological data often limit the accuracy of land surface hydrological models in estimating TWS. In this study, a multi-model-based framework was developed to predict TWS in China by 2050 using a Bayesian model averaging (BMA) method and GRACE satellite observations. Compared to GRACE observations, our BMA-based TWS anomaly (TWSA) estimations reduce root mean square errors by 10–16% and increase correlation coefficients by 26–46% over semi-humid and semi-arid basins than simple arithmetical averaging for the validation period (2008–2016). At the same time, BMA shows decreasing root mean square differences (10–12%) over humid basins. The calibrated BMA weights were then applied to future projections of TWSA under two Representative Concentration Pathways (RCP): RCP 2.6 and RCP 6.0. The overall rate of TWSA for the future period (2021–2050) was detected with the same direction as that from past decades (2003–2016), but with larger decreasing values. Especially for the Haihe basin in North China, BMA-based TWSA would decrease faster by about 19% for RCP 2.6 and 26% for RCP 6.0. These results suggest a decreasing trend in future TWS over most of the basins in China due to combined effects of global warming and human activities, which suggests likely aggravated risk of water shortage and a growing need for adaptive water resources management.

Missing Hydrological Contribution to Sea Level Rise

AbstractOver the past decade, the rate of global mean sea level (GMSL) rise is about 3.5 mm/year. Terrestrial water/ice mass loss to the oceans and ocean volume expansion explain about 3.1 mm/year, indicating that the GMSL budget is not been fully understood. Past estimates from Gravity Recovery and Climate Experiment (GRACE) data have indicated that terrestrial water storage (TWS) is increasing and is thus a mitigating contributor to GMSL rise. However, TWS estimates from GRACE are uncertain mostly due to limitations in GRACE estimates of degree‐1 and degree‐2 order‐0 spherical harmonic coefficients. We obtain an improved estimate of the TWS contribution to GMSL change using revised GRACE estimates of these low‐degree coefficients. For the period 2005–2015, we find that TWS makes an additional contribution to GMSL rise of about 0.32 ± 0.02 mm/year, mostly associated with a TWS decrease. This revised estimate is sufficient to nearly balance the budget of GMSL rise.

Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins

The Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage. Attempts to decompose GRACE-based TWS signals into its different water storage layers, i.e., surface water storage (SWS), soil moisture, groundwater and snow, have shown that SWS is a principal component, particularly in the tropics, where major rivers flow over arid regions at high latitudes. Here, we demonstrate that water levels, measured with radar altimeters at a limited number of locations, can be used to reconstruct gridded GRACE-based TWS signals in the Amazon basin, at spatial resolutions ranging from 0.5 to 3, with mean absolute errors (MAE) as low as 2.5 cm and correlations as high as 0.98. We show that, at 3 spatial resolution, spatially-distributed TWS time series can be precisely reconstructed with as few as 41 water-level time series located within the basin. The proposed approach is competitive when compared to existing TWS estimates derived from physically based and computationally expensive methods. Also, a validation experiment indicates that TWS estimates can be extrapolated to periods beyond that of the model regression with low errors. The approach is robust, based on regression models and interpolation techniques, and offers a new possibility to reproduce spatially and temporally distributed TWS that could be used to fill inter-mission gaps and to extend GRACE-based TWS time series beyond its timespan.

Evaluating the uncertainty of terrestrial water budget components over High Mountain Asia.

This study explores the uncertainties in terrestrial water budget estimation over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations. The uncertainty in the water balance components of precipitation (P), evapotranspiration (ET), runoff(R), and terrestrial water storage (TWS) is significantly impacted by the uncertainty in the driving meteorology, with precipitation being the most important boundary condition. Ten gridded precipitation datasets along with a mix of model-, satellite-, and gauge-based products, are evaluated first to assess their suitability for LSM simulations over HMA. The datasets are evaluated by quantifying the systematic and random errors of these products as well as the temporal consistency of their trends. Though the broader spatial patterns of precipitation are generally well captured by the datasets, they differ significantly in their means and trends. In general, precipitation datasets that incorporate information from gauges are found to have higher accuracy with low Root Mean Square Errors and high correlation coefficient values. An ensemble of LSM simulations with selected subset of precipitation products is then used to produce the mean annual fluxes and their uncertainty over HMA in P, ET, and R to be 2.11±0.45, 1.26±0.11, and 0.85±0.36 mm per day, respectively. The mean annual estimates of the surface mass (water) balance components from this model ensemble are comparable to global estimates from prior studies. However, the uncertainty/spread of P, ET, and R is significantly larger than the corresponding estimates from global studies. A comparison of ET, snow cover fraction, and changes in TWS estimates against remote sensing-based references confirms the significant role of the input meteorology in influencing the water budget characterization over HMA and points to the need for improving meteorological inputs.