Terrestrial Water Storage Anomalies Research Articles

Information content of soil hydrology in a west Amazon watershed as informed by GRACE

Abstract. 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.

Assessing changes in total water storage in two large freshwater lake basins of China

AbstractThe quantitative assessment of changes in total water storage (TWS) is of great significance for regional water resources management in basins with large lakes. Here, the basins of Poyang Lake and Dongting Lake, the two largest freshwater lakes in China, were selected to assess TWS changes using multi‐source datasets. The Gravity Recovery and Climate Experiment (GRACE) TWS anomalies (TWSA) increased by 8.8 mm a−2 (p < 0.01) and 8.3 mm a−2 (p < 0.01) from 2003 to 2016 for the Poyang Lake Basin (PYLB) and Dongting Lake Basin (DTLB), respectively. Four major components, namely lake water storage, soil moisture storage, groundwater storage and reservoir storage, contributed positively to the TWSA increase in the two lake basins. The TWSA increase was dominated by groundwater storage increase (52.6%) over PYLB and by reservoir storage increase (47.4%) over DTLB. The increases in soil moisture storage contributed 22.4% and 27.6% to TWSA changes for PYLB and DTLB, respectively, which were mainly caused by increases in precipitation. The contributions of lake water storage to TWSA increases were only 4.3% and 1.3% in PYLB and DTLB, respectively, indicating that the impacts of water storage in natural lakes on TWS were far lower than those of reservoirs, even in basins with large lakes. The results highlight that reservoir and groundwater storage play crucial roles in TWS partitioning, suggesting that attention should be paid to the two components of land surface and hydrological simulations in highly populated lake basins under global change.

The importance of vegetation in understanding terrestrial water storage variations

Abstract. So far, various studies have aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way that vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon, and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth-observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake, and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff (Q) estimates in a multi-criteria calibration approach. We assess the implications of including varying vegetation characteristics on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment in which vegetation parameters vary in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but explicitly including varying vegetation data leads to even better performance and more physically plausible parameter values. The largest improvements regarding TWS and ET are seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of different soil water storage components to the TWS variations. This suggests an important role of the representation of vegetation in hydrological models for interpreting TWS variations. Our simulations further indicate a major effect of deeper moisture storages and groundwater–soil moisture–vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation–water interactions.

Understanding the impact of climatic variability on terrestrial water storage in the Qinghai-Tibet Plateau of China

ABSTRACT Monitoring terrestrial water storage anomalies (TWSA) is essential for better understanding the influences of climatic variability on hydrological cycles. Here we use Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-on satellite data and meteorological observations to analyse the inter-annual trends of TWSA across the Qinghai-Tibet Plateau (QTP) during 2003–2020 and investigate the relationships between climatic variability and annual changes in TWSA. Results indicate that TWSA across the QTP generally decreased, at a rate of −0.5 ± 1.4 mm/year during 2003–2020, which mainly arises from the coupled effects of Potential evapotranspiration (PET) and precipitation. In terms of the effects of climatic variability, annual changes in TWSA show a negative correlation coefficient (r = −0.76) with PET, which is greater than that with precipitation (r = 0.67). The cross-wavelet transformation analysis revealed that the evolution of TWSA across the QTP is closely associated with the Atlantic Multidecadal Oscillation. Our conclusions can help decision makers to formulate appropriate policies for the assessment and management of water resources over the QTP.

Estimation of Evapotranspiration in the Yellow River Basin from 2002 to 2020 Based on GRACE and GRACE Follow-On Observations

Evapotranspiration (ET) plays an important role in the hydrological cycle of river basins. Studying ET in the Yellow River Basin (YRB) is greatly significant for the scientific management of water resources. Here, we made full use of the advantages of the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) gravity satellites for monitoring large-scale hydrological changes to calculate the terrestrial water storage anomaly (TWSA) and terrestrial water flux in the YRB from May 2002 to June 2020. Furthermore, combined with terrestrial water flux, precipitation, and runoff data, ET in the YRB was calculated based on the water budget equation and then compared with other traditional ET products. The mutation of annual mean ET was identified by the Mann–Kendall trend test method, and the seasonal and interannual variations of ET were explored. ET was closely related to precipitation. Annual mean ET exhibited a sudden change in 2011, with an insignificant downward trend from 2003 to 2010, followed by an increasing trend from 2011 to 2019, particularly after 2016. Compared with the traditional ET monitoring methods and products, the ET estimated by GRACE/GRACE-FO observations provides a new way to effectively obtain continuous and reliable ET data in a wide range of river basins.

Bridging the gap between GRACE and GRACE-FO using a hydrological model

Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), two successive satellite-based missions starting in 2002, have provided an unprecedented way of measuring global terrestrial water storage anomalies (TWSA). However, a temporal gap exists between GRACE and GRACE-FO products from July 2017 to May 2018, which introduces bias and uncertainties in TWSA calculations and modeling. Previous studies have incorporated hydroclimatic factors as predictors for filling the gap, but most of them utilized artificial intelligence or pure statistical models that generally de-trended TWSA and had no physical foundation. Thus, a physically-based reconstruction is required for increasing robustness. In this study, we bridge the temporal gap by developing an empirical hydrological model. The “abcd” model, a T-based snow component, and linear correction are utilized to represent runoff generation, snow dynamics, and long-term trends. The testing results indicate that our hydrological model can successfully reconstruct TWSA in tropical, temperature, and continental climates, although further improvement is needed for arid climates. Our reconstruction for the gap achieves high accuracy and robustness as shown by the evaluations against sea-level budget and GLDAS-derived TWSA. Compared to previous studies using artificial intelligence or statistical techniques, our hydrological model performs similarly in the gap filling but does not involve de-trended or de-seasonalized transformations, which will facilitate the combination of GRACE and GRACE-FO products and improve the physical understanding of global TWSA.

Uncertainties of GRACE‐Based Terrestrial Water Storage Anomalies for Arbitrary Averaging Regions

AbstractThe application of terrestrial water storage (TWS) data observed with GRACE and GRACE‐FO often requires realistic uncertainties. For gridded TWS data, this requires the knowledge of the covariances, which can be derived from the formal, i.e., formally estimated in the parameter estimation, variance‐covariance matrix provided together with the Stokes coefficients. However, the propagation of monthly variance‐covariance matrices to TWS data is computationally expensive, so we apply a spatial covariance model for TWS data. The covariance model provides non‐homogeneous (location depending), non‐stationary (time depending), and anisotropic (orientation depending) covariances between any two given points. Further, the model accommodates wave‐like behavior of East‐West‐directed covariances, which residuals of GRACE striping errors can cause. The main application of such spatial covariances is the estimation of uncertainties for mean TWS time series for arbitrary regions such as river basins. Alternatively, regional uncertainties can be derived from the above mentioned formal variance‐covariance matrices of the Stokes coefficients. This study compares modeled basin uncertainties for GFZ RL06 and ITSG‐Grace2018 TWS data with the formal basin uncertainties from the ITSG‐Grace 2018 solution. The modeled and formal uncertainties fit both in the spatial and temporal domain. We further evaluate the modeled uncertainties by comparison to empirical uncertainties over arid regions. Here, again the appropriateness of the modeled uncertainties is shown. The results, namely the TWS uncertainties for global river basins, are available via the GravIS portal. Further, we provide a Python toolbox, which allows computing uncertainties and covariance matrices.

Seasonal Amplitude of Water Storage Variations of the Yangtze–Huai Plain Lake Group: Implicaion for Floodwater Storage Capacity

Lakes are of significant importance in regulating floods and providing water sources. The seasonal water storage variations for the plain lake group in the Yangtze–Huai River Basin (YHRB) are significant for alleviating flood pressure and regulating runoff. However, to date, the seasonal amplitude of lake water storage variations and its capacity of buffering floodwater in the YHRB is not quantified well and remains to be investigated comprehensively. To advance the understanding of such a critical scientific issue, the water level data of the plain lake group (area>100 km2, 29 lakes) in the YHRB is collected from multi-source data between 1990 and 2020. Using lake inundation area obtained from Global Surface Water and water level variations, water storage dynamics for the plain lake group are quantified. Furthermore, this study also uses the Gravity Recovery and Climate Experiment (GRACE) products to analyze the terrestrial water storage anomalies (TWSA) in the whole basin. The results indicate that the seasonal amplitude of water level change and water storage variation of the plain lake group are 2.80 ± 0.71 m and 37.38 ± 14.19 Gt, respectively. Poyang and Dongting Lakes, two lakes that maintain the natural connection with the Yangtze River, have the most substantial seasonal amplitude in the hydrological situation. The amplitude in water level and water storage in Poyang Lake is 9.53 ± 2.02 m and 14.13 ± 5.54 Gt respectively, and that in Dongting Lake is 7.39 ± 1.29 m and 7.31 ± 3.42 Gt respectively. The contribution of seasonal variation of water storage for large plain lakes to TWSA in the YHRB is approximately 33.25%, fully reflecting these lake’s imperative position in the YHRB. This study is expected to enhance the scientific understanding of the seasonal hydrologic regime for the large lakes in the YHRB and contribute to the management of flood risks and water resources in East China.

Estimating the added value of GRACE total water storage and uncertainty quantification in seasonal streamflow forecasting

ABSTRACT Seasonal streamflow prediction is vital for water resources management and disaster mitigation. Multiple linear regression (MLR), as the simplest yet effective statistical model, is widely used in hydrological forecasting. To investigate the added value of incorporating GRACE total water storage anomalies (TWSA) in seasonal streamflow prediction and quantifying the uncertainty in model residuals, this study constructed four MLR models, namely a benchmark model, TWSA-incorporated model, uncertainty quantified model and synthetic model, to predict monthly streamflow in Yarlung Zangbo River, Upper Jinshajiang River, Xiangjiang River and Lanjiang River with lead times from 1 to 11 months. The results show that MLR models perform fairly well, with correlation coefficients greater than 0.8 and Nash-Sutcliffe efficiency coefficients up to 0.73. TWSA contributes moderately to forecast skill, whilst about 15% of the forecast skill is attributed to uncertainty quantification. The synergy of TWSA and uncertainty quantification brings basin-varying contributions, ranging from 5% to 26%.

Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

Densely distributed Global Navigation Satellite System (GNSS) stations can invert the terrestrial water storage anomaly (TWSA) with high precision. However, the uneven distribution of GNSS stations greatly limits the application of TWSA inversion. The purpose of this study was to compensate for the spatial coverage of GNSS stations by simulating the vertical deformation in unobserved grids. First, a new deep learning weight loading inversion model (DWLIM) was constructed by combining the long short-term memory (LSTM) algorithm, inverse distance weight, and the crustal load model. DWLIM is beneficial for improving the inversion accuracy of TWSA based on the GNSS vertical displacement. Second, the DWLIM-based and traditional GNSS-derived TWSA methods were utilized to derive TWSA over mainland China. Furthermore, the TWSA results were compared with the TWSA solutions of the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) model. The results indicate that the maximum Pearson’s correlation coefficient (PCC), Nash–Sutcliffe efficiency (NSE) coefficient, and root mean square error (RMSE) equal 0.81, 0.61, and 2.18 cm, respectively. The accuracy of DWLIM was higher than that of the traditional GNSS inversion method according to PCC, NSE, and RMSE, which were increased by 67.11, 128.15, and 22.75%. The inversion strategy of DWLIM can effectively improve the accuracy of TWSA inversion in regions with unevenly distributed GNSS stations. Third, this study investigated the variation characteristics of TWSA based on DWLIM in 10 river basins over mainland China. The analysis shows that the TWSA amplitudes of Songhua and Liaohe River basins are significantly higher than those of the other basins. Moreover, TWSA sequences in each river basin contain annual seasonal signals, and the wave peaks of TWSA estimates emerge between June and July. Overall, DWLIM provides a useful measure to derive TWSA in regions where GNSS stations are uneven or sparse.

Projected changes in terrestrial water storage and associated flood potential across the Yangtze River basin

Terrestrial water storage is a crucial component in water cycle and plays an important role in flood formations process, particularly in a changing environment. In this study, we aim to examine the future variation of terrestrial water storage anomaly (TWSA) and associated flood potential in one of the most flood-prone regions, the Yangtze River basin in China. Using the Gravity Recovery and Climate Experiment (GRACE) data, we perform bias correction for seven general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 under three Shared Socio-economic Pathway (SSP) scenarios: SSP126, SSP245, and SSP585. The spatiotemporal characteristics of changes in future Flood Potential Index are projected and compared between the near (2031–2060) and far (2071–2100) future with reference to the historical period (1985–2014). The results show that GCMs-simulated TWSA generally agrees well with the GRACE results after downscaling and bias correction with the average correlation coefficient of 0.86, Nash-Sutcliffe efficiency of 0.73 and the root mean square error of 21.68 mm. We found that the total variance of projected TWSA is mainly sourced from the internal variability and model uncertainties, while the uncertainties in scenarios contribute relatively less. Moreover, the flood potential is projected to decline during the near future under various scenarios and even lower during the far future under SSP585 scenario. Our findings provide implications for flood control and management under climate change over high flood risk regions worldwide.

Water Budget Closure in the Upper Chao Phraya River Basin, Thailand Using Multisource Data

Accurate quantification of the terrestrial water cycle relies on combinations of multisource datasets. This analysis uses data from remotely sensed, in-situ, and reanalysis records to quantify the terrestrial water budget/balance and component uncertainties in the upper Chao Phraya River Basin from May 2002 to April 2020. Three closure techniques are applied to merge independent records of water budget components, creating up to 72 probabilistic realizations of the monthly water budget for the upper Chao Phraya River Basin. An artificial neural network (ANN) model is used to gap-fill data in and between GRACE and GRACE-FO-based terrestrial water storage anomalies. The ANN model performed well with r ≥ 0.95, NRMSE = 0.24 − 0.37, and NSE ≥ 0.89 during the calibration and validation phases. The cumulative residual error in the water budget ensemble mean accounts for ~15% of the ensemble mean for both the precipitation and evapotranspiration. An increasing trend of 0.03 mm month−1 in the residual errors may be partially attributable to increases in human activity and the relative redistribution of biases among other water budget variables. All three closure techniques show similar directions of constraints (i.e., wet or dry bias) in water budget variables with slightly different magnitudes. Our quantification of water budget residual errors may help benchmark regional hydroclimate models for understanding the past, present, and future status of water budget components and effectively manage regional water resources, especially during hydroclimate extremes.

A Spatial Downscaling Methodology for GRACE Total Water Storage Anomalies Using GPM IMERG Precipitation Estimates

A downscaling framework for coarse resolution Gravity Recovery and Climate Experiment (GRACE) Total Water Storage Anomaly (TWSA) data is described, exploiting the observations of precipitation from the Global Precipitation Measurement (GPM) mission, using the Integrated Multi-satellite Retrievals for GPM (IMERG). Considering that the major driving force for changes in TWS is precipitation, we tested our hypothesis that coarse resolution, i.e., 1°, GRACE TWSA can be effectively downscaled to 0.1° using GPM IMERG data. The algorithm for the downscaling process comprises the development of a regression equation at the coarse resolution between the GRACE and GPM IMERG data, which is then applied at the finer resolution with a subsequent residual correction procedure. An ensemble of GRACE data from three processing centers, i.e., GFZ, JPL and CSR, was used for the time period from June 2018 until March 2021. To verify our downscaling methodology, we applied it with GRACE data from 2005 to 2015, and we compared it against modeled TWSA from two independent datasets in the Thrace and Thessaly regions in Greece for the same period and found a high performance in all examined metrics. Our research indicates that the downscaled GRACE observations are comparable to the TWSA estimated with hydrological modeling, thus highlighting the potential of GRACE data to contribute to the improvement of hydrological model performance, especially in ungauged basins.

Dynamical variations of the terrestrial water cycle components and the influences of the climate factors over the Aral Sea Basin through multiple datasets

Assessments of the regional terrestrial water variabilities are important for improving our knowledges of the complex hydroclimate system and providing scientific information in regional water resource management under a changing environment due to climate change and intensified human activities. The Aral Sea Basin has experienced a serious ecological crisis which is majorly caused by the water resources due to the excessive water utilization. Therefore, in this study, we examine the long-term linear trend and variability of the terrestrial water cycle components based on multiple datasets over the Aral Sea Basin during 2003–2016. The terrestrial water cycle components include precipitation, evapotranspiration (ET), terrestrial water storage anomaly (TWSA), terrestrial water storage change (TWSC), runoff (R), soil moisture (SM) and groundwater. Major results show that increased trends of the annual precipitation and ET are observed during 2003–2016. For TWSA, Gravity Recovery and Climate Experiment (GRACE) derived datasets have significantly decreased trends with the values from −0.47 mm/mon to −0.29 mm/mon which reveal the serious terrestrial water depletion. Runoff, soil moisture and groundwater have the decreasing linear trends derived from multiple datasets. For the impacts of the climate factors on TWSA and TWSC, precipitation is the main climate factor with the significantly positive impacts. These results help us to have a better understanding of the complex hydrological process over the Aral Sea Basin, and provide a reliable scientific basic for police maker in the water resource management to achieve a sustainable development goal over the regions of the One Belt and One Road.

Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap

The monthly terrestrial water storage anomaly (TWSA) observations during the gap period between the Gravity Recovery and Climate Experiment (GRACE) satellite and its Follow-On (GRACE-FO) are missing, leading to discontinuity in the time series, and thus, impeding full utilization and analysis of the data. Despite previous efforts undertaken to tackle this issue, a gap-filling TWSA product with desirable accuracy at a global scale is still lacking. In this study, a straightforward and hydroclimatic data-driven Bayesian convolutional neural network (BCNN) is proposed to bridge this gap. Benefiting from the excellent capability of BCNN in handling image data and the integration of recent deep learning advances (including residual-skip connections and spatial-channel attentions), the proposed method can automatically extract informative features for TWSA predictions from multiple predictor data. The BCNN predictions are compared with reanalyzed/simulated TWSA, Swarm solution, and the TWSA prediction products generated by three recent studies, using commonly used accuracy metrics. Results demonstrate BCNN’s superior performance to obtain higher-quality TWSA predictions, particularly in relatively arid regions. Additionally, a comparison with two independent datasets at the basin scale further suggests that the BCNN-infilled TWSA is reliable to bridge the gap and enhance data consistency. Our gap-filling product can ultimately contribute to correcting the bias in long-term trend estimates, maintaining the continuity of TWSA time series and thus benefiting subsequent applications desiring continuous data records.

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.

Insights into hydrological drought characteristics using GNSS-inferred large-scale terrestrial water storage deficits

The hydrological loading displacements measured by continuous Global Navigation Satellite System (GNSS) networks can provide critical constraints on total terrestrial water storage (TWS) anomalies. We invert sparsely distributed GNSS vertical positions for daily large-scale water heights based on Slepian basis functions and devise a novel GNSS-based drought severity index (GNSS-DSI) dataset for drought characterization in Brazil. The spatiotemporal patterns of GNSS-inferred water estimates agree with the TWS observations derived from Gravity Recovery and Climate Experiment (GRACE) spherical harmonic solutions. Both GNSS and GRACE capture notable annual water oscillations in the Amazon River Basin, with an annual amplitude close to 500 mm, larger than that of 100–200 mm in the other geographical divisions. The newly-developed monthly GNSS-DSI time series correlate well with the well-accepted GRACE-DSI dataset, and 95% of stations feature moderate-to-strong correlations (greater than 0.50) between these two DSI data sets. The new drought monitoring tool solely based on GNSS-inferred water storage deviations succeeds in identifying the well-documented historical droughts and provides a quantitative characterization of these drought extremes in the four large river basins in Brazil. Our results demonstrate that a sparsely instrumented continuous GNSS network could be taken as an independent tool to remotely monitor large-scale TWS variations and to quantitatively characterize regional-scale hydrological extremes.

Impact of variability in the hydrological cycle components on vegetation growth in an alpine basin of the southeastern Tibet Plateau, China

Abstract Vegetation is affected by hydrological cycle components that have altered under the influence of climate change. Therefore, it is necessary to investigate the impact of hydrological cycle components on regional vegetation growth, especially in alpine regions. In this study, we employed multiple satellite observations to comprehensively investigate the spatial heterogeneity of hydrological cycle components in the Yarlung Zangbo River (YZR) basin for the period 1982–2014 and to determine the underlying mechanisms driving regional vegetation growth. Results showed that the normalized difference vegetation index (NDVI) values during May–October were high, and the NDVI values increased from the upper reaches of the YZR to its lower reaches, reflecting the enhancement of vegetation growth. Annual precipitation, precipitation-actual evapotranspiration (AET), and snow water equivalent (SWE) all affect terrestrial water storage in the YZR basin through changes in soil moisture (SM), i.e., SM is the intermediate variable. Seasonal variability of vegetation is controlled mainly by precipitation, temperature, AET, SM anomaly, and SWE. Groundwater storage anomalies (GWA) and terrestrial water storage anomalies (TWSA) were not reliable indicators of vegetation growth in the YZR basin and the midstream and downstream regions. The effects of GWA and TWSA on vegetation occurred in the upstream region.

The use of triple collocation approach to merge satellite- and model-based terrestrial water storage for flood potential analysis

Terrestrial water storage anomaly (TWSA) retrieved from the Gravity Recovery and Climate Experiment (GRACE) mission can be used as a good indicator of flood potential, but it is limited to regional analysis due to the coarse resolution (∼300 km). This study proposed an optimal least square merging scheme to spatially downscale GRACE-based TWSA via integrating it with model-based TWSA estimates on to a finer resolution (0.125°) with associated uncertainty estimates. TWSA products estimated from GRACE, an advanced Land Surface Model (LSM), and reanalysis data were used in the merging process. Error uncertainties in each input data sets and the merged TWSA product were calculated based on the triple collocation approach. The merged TWSA was subsequently used to compute the flood potential index and results were compared against measured river discharge and historical flood records. Results showed that the merged TWSA provides a better representation of the spatial variability of TWSA at a sub-regional scale relative to GRACE-based TWSA along with improved uncertainties. Merged TWSA also showed improved capability in capturing flood events that occurred at sub-regional to local scales relative to GRACE-based TWSA when compared to historical flood records.

Land deformation and sinkhole occurrence in response to the fluctuations of groundwater storage: an integrated assessment of GRACE gravity measurements, ICESat/ICESat-2 altimetry data, and hydrologic models

ABSTRACT Uncontrolled extraction of water from groundwater aquifers causes groundwater depletion, which in turn triggers the formation of sinkholes in many parts of the world. Monitoring and detection of these geomorphologic features are of utmost importance and priority for the decision-makers to minimize significant environmental as well as socio-economic implications of land deformation. In this study, a systematic approach is proposed to investigate the spatio-temporal associations of groundwater storage changes with sinkhole evolution and land deformation by using a number of remotely sensed and modeled data as well as in-situ observations. The proposed approach is implemented and tested in Konya Closed Basin (KCB), Turkey, which is one of the most critical areas in central Turkey concerning sinkhole formation. The results of GRACE (Gravity Recovery and Climate Experiment) estimates suggest that there is a descending trend in the temporal variations of TWSA (Terrestrial Water Storage Anomalies) and GWSA (Groundwater Storage Anomalies) over KCB with an average storage depletion of 4.12 ± 0.34 cm/yr and 3.40 ± 0.61 cm/yr, respectively. The analysis of land deformation from ICESat/ICESat-2 (Ice, Cloud, and Land Elevation Satellite) altimetry data also indicates a descending trend with an estimated average vertical displacement of 5 cm/yr for the study area, which seems to be in rational accord with the sinkhole evolution over KCB. The results further suggest that the sinkhole evolution over KCB has an acceptable association with the variations of groundwater storage, groundwater use, and precipitation. Compared with previous works implemented over KCB, the findings of this study manifest a good performance for the proposed methodology. The integrated analysis of GRACE gravity measures, ICESat/ICESat-2 altimetry data, modeled groundwater abstraction, and in-situ observations of precipitation yields acceptable results for the detection and monitoring of sinkhole events provided that appropriate distribution of the elevation points from ICESat/ICESat-2 is accessible for the given study area.