Predicting Streamflow Elasticity Based on Percolation Theory and Ecological Optimality

Deriving scaling factors using a global hydrological model to restore GRACE total water storage changes for China's Yangtze River Basin

Aiming at the Terrestrial Water Storage(TWS) changes in the Amazon River basin, this article uses the coordinate time series data of the Global Navigation Satellite System (GNSS), adopts the Variational Mode Decomposition and Bidirectional Long and Short Term Memory(VMD-BiLSTM) method to extract the vertical crustal deformation series, and then adopts the Principal Component Analysis(PCA) method to invert the changes of terrestrial water storage in the Amazon Basin from July 15, 2012 to July 25, 2018. Then, the GNSS inversion results were compared with the equivalent water height retrieved from Gravity Recovery and Climate Experiment (GRACE) data. The results show that (1) the extraction method proposed in this article has better denoising effect than the traditional method; (2) the surface hydrological load deformation can be well calculated using GNSS coordinate vertical time series, and then the regional TWS changes can be inverted, which has a good consistency with the result of GRACE inversion of water storage, and has almost the same seasonal variation characteristics; (3) There is a strong correlation between TWS changes retrieved by GNSS based on surface deformation characteristics and water mass changes calculated by GRACE based on gravitational field changes, but GNSS satellite’s all-weather measurement results in a finer time scale compared with GRACE inversion results. In summary, GNSS can be used as a supplementary technology for monitoring terrestrial water storage changes, and can complement the advantages of GRACE technology.

The Gravity Recovery and Climate Experiment, GRACE, will enable the recovery of monthly estimates of changes in water storage, on land and in the ocean, avenged over arbitrary regions having length scales of a few hundred km and larger. These data will allow the examination of changes in the distribution of water in the ocean, in snow and ice on polar ice sheets, and in coniinental waler and snow storage. Extracting changes in waler storage from the GRACE dataset requires the use of averaging kernels which can isolate a particular region. To estimate the accuracy to which continental water storage changes in a few representative regions may be recovered, we construct a synthetic GRACE dataset from global, gridded models of surface-mass variability. We find that regional changes in water storage can be recovered with rms error less than 1 cm of equivalent water thickness, for regions having areas of 4 × l05 km2 and larger. Signals in smaller regions may also be recovered; however, interpretations of such results require a careful consideration of model resolution, as well as the nature of the averaging kernel.KeywordsAverage KernelMississippi River BasinEquivalent Water ThicknessGrace DataSnow StorageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Water stored deep in the soil profile is the primary bio-available reservoir for regional vegetation in the semiarid Loess Plateau of China. However, the planting of introduced shrubs over many years as part of the “Grain to Green Program (GGP)” has consistently lead to dried soil in areas with severe water scarcity. Knowledge of soil water storage (SWS) changes within deep profiles in water-deficient regions is critical for the sustainable development of vegetation restoration. Caragana korshinskii K. (CK) and Hippophae rhamnoides L. (HR) are widely planted in the Loess Plateau to control soil erosion. We selected these two shrubs for a study on variations in deep soil water (100–500 cm) and identified the main factors affecting deep soil water storage replenishment (SWSR) during their growing seasons. The results indicated that the mean SWS at 100–500 cm depth under HR was significantly higher than that under CK at both the beginning (352.74 mm for CK and 644.79 mm for HR) and end of the growing season (311.95 mm for CK and 529.05 mm for HR) (p < 0.01). In these ecosystems, SWS was only recharged below 340 cm under CK, which was due to vegetation characteristics. Under HR, however, soil water consumption exceeded recharge throughout the whole 100–500 cm profile. The SWSR at the 100–340 cm depth was mainly affected by sand content, which explained 28% of the variability of SWSR. At the 340–500 cm depth, the variability in SWSR was due to vegetation type. Therefore, expansion of the GGP should pay more attention to both soil water conditions and influencing factors, including appropriate vegetation selection and the altering of the microtopography.

Understanding linkages between global climate indices and terrestrial water storage changes over Africa using GRACE products

Long-term groundwater variations in Northwest India from satellite gravity measurements

<p>Reliable predictions of terrestrial water storage (TWS) changes for the next couple of years would be extremely valuable for agriculture and water management. Decadal predictions have already shown to be meaningful for predicting e.g. sea surface and air temperature, but have not yet been intensively investigated regarding TWS. Here we evaluate decadal hindcasts of TWS related variables from an ensemble of five CMIP5 (Coupled Model Intercomparison Project Phase 5) climate models against a TWS data set that is based on GRACE (Gravity Recovery And Climate Experiment) satellite observations.</p><p>As the overlap time span of 9 years for the model time series and GRACE observations is not long enough for a robust comparison, we also use a GRACE-based reconstruction of TWS utilizing precipitation and temperature data sets (Humphrey and Gudmundsson, 2019) available back to the year 1900. Thus we are able to compare the full 41 year (1970-2011) time span covered by CMIP5 decadal predictions to the TWS reconstruction. Correlations and root mean squared deviations (RMSD) are calculated for yearly global averages and for individual climate zones. Furthermore, we derive global maps of correlations and RMSD.</p><p>We find that at least for the first two prediction years the decadal model experiments clearly outperform the classical climate projections, regionally even for the third year. However, the spread among the models is large and absolute similarities between model output and GRACE TWS reconstructions are quite low.</p><p>We also perform a preliminary skill assessment for the first CMIP6 decadal hindcasts publicly available, finding a slightly reduced skill for the first forecast year in comparison to the CMIP5 models, while for the second forecast year an improvement is seen. This result is generally encouraging, but requires confirmation as soon as more CMIP6 decadal hindcasts become available.</p><p>Humphrey, V., Gudmundsson, L., 2019. GRACE-REC: a reconstruction of climate-driven water storage changes over the last century. Earth System Science Data Discussions 1–41. https://doi.org/10.5194/essd-2019-25</p>

Investigating terrestrial water storage changes in Southwest China by integrating GNSS and GRACE/GRACE-FO observations

The continuously distributed changes in water storage in hydrosphere deform the shape of the Earth’s surface, which can be recorded by GPS position time series. However, GPS stations that exhibit poroelastic behavior located on aquifer systems are excluded from largely previous studies so that terrestrial water storage variations can be estimated by elastic loading, which leads to biases in results. We proposed a novel approach to classify GPS stations whose vertical displacement time series are significantly correlated with hydrological loading variations to construct hydro-geodesy datasets, including elastic response (positive), poroelastic response (negative), and aquifer compaction. Using the wavelet analysis method, we further identified 569 GPS vertical displacement time series from California provided by the Nevada Geodetic Laboratory between 2007 and 2017 into the pre-defined temporal-scales of long-term, seasonal, and short-term. We calculated and evaluated elastic deformation induced by hydrological loading variations, including GRACE, WaterGAP, GLDAS, NLDAS, ERA5, and ERA5-land, and the HYDL product provided by GFZ. The results show that most/several GPS stations located outside/within the Central Valley are under the control of the elastic response. We also used a poroelastic half-space model to validate that most GPS stations located within the Central Valley are simultaneously affected by surface subsidence and controlled by poroelastic response. Our results show that the hydro-geodetic datasets we constructed enable the use of previously and widely neglected GPS stations, such as those that may observe poroelastic response and those affected by surface subsidence, to accurately monitor changes in terrestrial water storage during droughts and floods.

The quantitative study of vadose zone water (i.e., the water contained in the rock formation between the ground surface and the groundwater level) is of vital importance for understanding the groundwater flow mechanism, mass exchange, and circulation. However, the vadose zone is closely related to the saturated zone, and it is difficult to directly observe the vadose zone water, causing the quantitative separation of the vadose zone water storage to be a challenging problem. Considering the insufficient spatial resolution of Gravity Recovery and Climate Experiment (GRACE), we draw on the practice of converting the observed groundwater level changes into the water storage changes in the study of groundwater storage changes using GRACE data, and propose a new method for implementing the quantitative separation of vadose zone water storage changes based on a single superconducting gravimeter and groundwater level observation data. This new method is validated with measurement data collected using a superconducting gravimeter (GWR-C032) at the National Geodetic Observation Station in Wuhan, China (Wuhan SG Station) and groundwater level observation data obtained during the same period. The calculation results show that the changes in the vadose zone water storage obtained using the superconductive gravity technique are in good agreement with that obtained using the local hydrological modeling method, indicating that the quantitative separation of the changes in the local vadose zone water storage can be achieved using superconductive gravity and groundwater level observations. During the study period from May 2008 to April 2010, the peak-to-peak changes of the vadose zone water storage at Wuhan SG Station reached 1580 mm, and the seasonal variations were the inverse of the variations of the total groundwater storage changes and the saturated zone water storage changes, indicating that the vadose zone plays a pronounced role in slowing down the total groundwater storage changes.

This paper is devoted to the first results from the GHYRAF (Gravity and Hydrology in Africa) experiment conducted since 2008 in West Africa and is aimed at investigating the changes in water storage in different regions sampling a strong rainfall gradient from the Sahara to the monsoon zone. The analysis of GPS vertical displacement in Niamey (Niger) and Djougou (Benin) shows that there is a clear annual signature of the hydrological load in agreement with global hydrology models like GLDAS. The comparison of GRACE solutions in West Africa, and more specifically in the Niger and Lake Chad basins, reveals a good agreement for the large scale annual water storage changes between global hydrology models and space gravity observations. Ground gravity observations done with an FG5 absolute gravimeter also show signals which can be well related to measured changes in soil and ground water. We present the first results for two sites in the Sahelian band (Wankama and Diffa in Niger) and one (Djougou in Benin) in the Sudanian monsoon region related to the recharge–discharge processes due to the monsoonal event in summer 2008 and the following dry season. It is confirmed that ground gravimetry is a useful tool to constrain local water storage changes when associated to hydrological and subsurface geophysical in situ measurements.

Drought monitoring is important for characterizing the timing, extent, and severity of drought for effective mitigation and water management. Presented here is a novel satellite-based drought severity index (DSI) for regional monitoring derived using time-variable terrestrial water storage changes from the Gravity Recovery and Climate Experiment (GRACE). The GRACE-DSI enables drought feature comparison across regions and periods, it is unaffected by uncertainties associated with soil water balance models and meteorological forcing data, and it incorporates water storage changes from human impacts including groundwater withdrawals that modify land surface processes and impact water management. Here, the underlying algorithm is described, and the GRACE-DSI performance in the continental United States during 2002–14 is evaluated. It is found that the GRACE-DSI captures documented regional drought events and shows favorable spatial and temporal agreement with the monthly Palmer Drought Severity Index (PDSI) and the U.S. Drought Monitor (USDM). The GRACE-DSI also correlates well with a satellite-based normalized difference vegetation index (NDVI), indicating sensitivity to plant-available water supply changes affecting vegetation growth. Because the GRACE-DSI captures changes in total terrestrial water storage, it complements more traditional drought monitoring tools such as the PDSI by providing information about deeper water storage changes that affect soil moisture recharge and drought recovery. The GRACE-DSI shows potential for globally consistent and effective drought monitoring, particularly where sparse ground observations (especially precipitation) limit the use of traditional drought monitoring methods.

<p>There is considerable concern about water depletion caused by climate extremes (e.g., drought) and human water use in the U.S. and globally. Major U.S. aquifers provide an ideal laboratory to assess water storage changes from GRACE satellites because the aquifers are intensively monitored and modeled. The objective of this study was to assess the relative importance of climate extremes and human water use on GRACE Total Water Storage Anomalies in 14 major U.S. aquifers and to evaluate the reliability of the GRACE data by comparing with groundwater level monitoring (~-23,000 wells) and regional and global models. We quantified total water and groundwater storage anomalies over 2002 – 2017 from GRACE satellites and compared GRACE data with groundwater level monitoring and regional and global modeling results.  </p> <p>The results show that water storage changes were controlled primarily by climate extremes and amplified or dampened by human water use, primarily irrigation. The results were somewhat surprising, with stable or rising long-term trends in the majority of aquifers with large scale depletion limited to agricultural areas in the semi-arid southwest and southcentral U.S. GRACE total water storage in the California Central Valley and Central/Southern High Plains aquifers was depleted by drought and amplified by groundwater irrigation, totaling ~70 km<sup>3</sup> (2002–2017), about 2× the capacity of Lake Mead, the largest surface reservoir in the U.S. In the Pacific Northwest and Northern High Plains aquifers, lower drought intensities were partially dampened by conjunctive use of surface water and groundwater for irrigation and managed aquifer recharge, increasing water storage by up to 22 km<sup>3</sup> in the Northern High Plains over the 15 yr period. GRACE-derived total water storage changes in the remaining aquifers were stable or slightly rising throughout the rest of the U.S.</p> <p>GRACE data compared favorably with composite groundwater level hydrographs for most aquifers except for those with very low signals, indicating that GRACE tracks groundwater storage dynamics. Comparison with regional models was restricted to the limited overlap periods but showed good correspondence for modeled aquifers with the exception of the Mississippi Embayment, where the modeled trend is 4x the GRACE trend. The discrepancy is attributed to uncertainties in model storage parameters and groundwater/surface water interactions. Global hydrologic models (WGHM-2d and PCR-GLOBWB-5.0 overestimated trends in groundwater storage in heavily exploited aquifers in the southwestern and southcentral U.S. Land surface models (CLSM-F2.5 and NOAH-MP) seem to track GRACE TWSAs better than global hydrologic models but underestimated TWS trends in aquifers dominated by irrigation.</p> <p>Intercomparing GRACE, traditional hydrologic monitoring, and modeling data underscore the importance of considering all data sources to constrain water storage changes.  GRACE satellite data have critical implications for many nationally important aquifers, highlighting the importance of conjunctively using surface-water and groundwater and managed aquifer recharge to enhance sustainable development.</p>

The uneven distribution of global navigation satellite system (GNSS) continuous stations in the Yellow River Basin, combined with the sparse distribution of GNSS continuous stations in some regions and the weak far-field load signals, poses challenges in using GNSS vertical displacement data to invert terrestrial water storage changes (TWSCs). To achieve the inversion of water reserves in the Yellow River Basin using unevenly distributed GNSS continuous station data, in this study, we employed the Tikhonov regularization method to invert the terrestrial water storage (TWS) in the Yellow River Basin using vertical displacement data from network engineering and the Crustal Movement Observation Network of China (CMONOC) GNSS continuous stations from 2011 to 2022. In addition, we applied an inverse distance weighting smoothing factor, which was designed to account for the GNSS station distribution density, to smooth the inversion results. Consequently, a gridded product of the TWS in the Yellow River Basin with a spatial resolution of 0.5 degrees on a daily scale was obtained. To validate the effectiveness of the proposed method, a correlation analysis was conducted between the inversion results and the daily TWS from the Global Land Data Assimilation System (GLDAS), yielding a correlation coefficient of 0.68, indicating a strong correlation, which verifies the effectiveness of the method proposed in this paper. Based on the inversion results, we analyzed the spatial–temporal distribution trends and patterns in the Yellow River Basin and found that the average TWS decreased at a rate of 0.027 mm/d from 2011 to 2017, and then increased at a rate of 0.010 mm/d from 2017 to 2022. The TWS decreased from the lower-middle to lower reaches, while it increased from the upper-middle to upper reaches. Furthermore, an attribution analysis of the terrestrial water storage changes in the Yellow River Basin was conducted, and the correlation coefficients between the monthly average water storage changes inverted from the results and the monthly average precipitation, evapotranspiration, and surface temperature (AvgSurfT) from the GLDAS were 0.63, −0.65, and −0.69, respectively. This indicates that precipitation, evapotranspiration, and surface temperature were significant factors affecting the TWSCs in the Yellow River Basin.

<p>In this study, we use temporal solutions of the Gravity Recovery and Climate Experiment (GRACE) satellite mission to study the surface mass variations of hydrological origin in North America. The most recent release (RL06) of GRACE Level 2 data from three processing centers (CSR, JPL, GFZ) and mascon products are used in a combination scheme to produce estimates of terrestrial water storage (TWS) changes for the period 2002–2016. The land hydrology signal is isolated from GRACE data by removing the contribution of two major non-hydrologic processes, i.e., the glacial isostatic adjustment (GIA) and the ice mass melting from the glaciated areas of Alaska, Greenland and the Canadian Arctic.</p><p>The examination of long-term TWS trends revealed strong signatures of the 2011–2015 droughts in California and Texas, as well as accumulation of TWS in the central part of North America. Negative long-term TWS trends associated with ice melting were found around the Hudson Bay region. The TWS changes are dominated by a strong annual and semi-annual signal with higher magnitude in Alaska and along the west coast of North America.</p><p>An additional study on the estimation of groundwater storage (GWS) changes is performed using the Global Land Data Assimilation System (GLDAS) model. The GLDAS data are pre-filtered using the same strategy as GRACE data to ensure spectral consistency between them. The general behavior of GWS agrees well with the TWS, especially in terms of positive long-term GWS trends in central North America and strong annual signal in Alaska. Positive GWS trends are also identified in the east US coast.</p>