A Preliminary Analysis of Lake Level and Water Storage Changes over Lakes Baikal and Balkhash from Satellite Altimetry and Gravimetry

Lakes Baikal and Balkhash are two of the world¡¦s major lakes affecting fresh water supplies in their catchments. Measurements from satellite altimetry (TOPEX/Poseidon, Jason-1 and -2), satellite gravimetry (GRACE) and a hydrological model (LDAS) are used to see the relationship between lake level change (LLC) and water storage change in these two lakes. At Lake Baikal, the average rate of LLC is negative for 1992 - 1998 and positive for 1998 - 2007, and the reversal of the LLC trend concurs with that of the temperature trend during the 1997 - 1998 El Nino. The rate of gravity change ranges from -0.5 to 0.5 ugal yr-1 with a low over the Tian Shan and a high over western Lake Baikal. Due to the climates over the two lakes, the phases of the annual gravity changes differ by up to 100 days. Using the rates of LLC and gravity changes, the ratios between the mass changes of the lake and its catchment over Lakes Baikal and Balkhash are estimated to 0.6 and 0.3, respectively. The result may help to establish water balance models over these two lakes

Evaluation of Groundwater Storage Variations Estimated from GRACE Data Assimilation and State-of-the-Art Land Surface Models in Australia and the North China Plain

The accurate knowledge of the groundwater storage variation (ΔGWS) is essential for reliable water resource assessment, particularly in arid and semi-arid environments (e.g., Australia, the North China Plain (NCP)) where water storage is significantly affected by human activities and spatiotemporal climate variations. The large-scale ΔGWS can be simulated from a land surface model (LSM), but the high model uncertainty is a major drawback that reduces the reliability of the estimates. The evaluation of the model estimate is then very important to assess its accuracy. To improve the model performance, the terrestrial water storage variation derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission is commonly assimilated into LSMs to enhance the accuracy of the ΔGWS estimate. This study assimilates GRACE data into the PCRaster Global Water Balance (PCR-GLOBWB) model. The GRACE data assimilation (DA) is developed based on the three-dimensional ensemble Kalman smoother (EnKS 3D), which considers the statistical correlation of all extents (spatial, temporal, vertical) in the DA process. The ΔGWS estimates from GRACE DA and four LSM simulations (PCR-GLOBWB, the Community Atmosphere Biosphere Land Exchange (CABLE), the Water Global Assessment and Prognosis Global Hydrology Model (WGHM), and World-Wide Water (W3)) are validated against the in situ groundwater data. The evaluation is conducted in terms of temporal correlation, seasonality, long-term trend, and detection of groundwater depletion. The GRACE DA estimate shows a significant improvement in all measures, notably the correlation coefficients (respect to the in situ data) are always higher than the values obtained from model simulations alone (e.g., ~0.15 greater in Australia, and ~0.1 greater in the NCP). GRACE DA also improves the estimation of groundwater depletion that the models cannot accurately capture due to the incorrect information of the groundwater demand (in, e.g., PCR-GLOBWB, WGHM) or the unavailability of a groundwater consumption routine (in, e.g., CABLE, W3). In addition, this study conducts the inter-comparison between four model simulations and reveals that PCR-GLOBWB and CABLE provide a more accurate ΔGWS estimate in Australia (subject to the calibrated parameter) while PCR-GLOBWB and WGHM are more accurate in the NCP (subject to the inclusion of anthropogenic factors). The analysis can be used to declare the status of the ΔGWS estimate, as well as itemize the possible improvements of the future model development.This work is funded by The University of Newcastle to support NASA’s GRACE and GRACE Follow-On projects as an international science team member to the missions

Assessing Performances of Multivariate Data Assimilation Algorithms with SMOS, SMAP, and GRACE Observations for Improved Soil Moisture and Groundwater Analyses

Multivariate data assimilation (DA) of satellite soil moisture (SM) and terrestrial water storage (TWS) observations has recently been used to improve SM and groundwater storage (GWS) simulations. Previous studies employed the ensemble Kalman approach in multivariate DA schemes, which assumes that model and observation errors have a Gaussian distribution. Despite the success of the Kalman approaches, SM and GWS estimates can be suboptimal when the Gaussian assumption is violated. Other DA approaches, such as particle smoother (PS), ensemble Gaussian particle smoother (EnGPS), and evolutionary smoother (EvS), do not rely on the Gaussian assumption and may be better suited to non-Gaussian error systems. The objective of this paper is to evaluate the performance of these four DA approaches (EnKS, PS, EnGPS, and EvS) in multivariate DA systems by assimilating satellite data from the Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery And Climate Experiment (GRACE) missions into the Community Atmosphere and Biosphere Land Exchange (CABLE) land surface model. The analyses are carried out in Australia’s Goulburn River catchment, where in situ SM and groundwater data are available to comprehensively validate the DA performance. Results show that all four DA approaches have outstanding performances and improve correlation coefficients of SM and GWS estimates by ~20% and 100%, respectively. The EvS outperforms the others, but its benefit is relatively marginal compared to Gaussian approaches (e.g., EnKS). This is due to the fact that SM and TWS error distributions in this study are close to Gaussian: a suitable condition for, e.g., EnKS, EnGPS. The robust performance of EvS appears to be the optimal approach for jointly assimilating multi-source hydrological observations to improve regional hydrological analyses

Assessing Performances of Multivariate Data Assimilation Algorithms with SMOS, SMAP, and GRACE Observations for Improved Soil Moisture and Groundwater Analyses

Multivariate data assimilation (DA) of satellite soil moisture (SM) and terrestrial water storage (TWS) observations has recently been used to improve SM and groundwater storage (GWS) simulations. Previous studies employed the ensemble Kalman approach in multivariate DA schemes, which assumes that model and observation errors have a Gaussian distribution. Despite the success of the Kalman approaches, SM and GWS estimates can be suboptimal when the Gaussian assumption is violated. Other DA approaches, such as particle smoother (PS), ensemble Gaussian particle smoother (EnGPS), and evolutionary smoother (EvS), do not rely on the Gaussian assumption and may be better suited to non-Gaussian error systems. The objective of this paper is to evaluate the performance of these four DA approaches (EnKS, PS, EnGPS, and EvS) in multivariate DA systems by assimilating satellite data from the Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery And Climate Experiment (GRACE) missions into the Community Atmosphere and Biosphere Land Exchange (CABLE) land surface model. The analyses are carried out in Australia’s Goulburn River catchment, where in situ SM and groundwater data are available to comprehensively validate the DA performance. Results show that all four DA approaches have outstanding performances and improve correlation coefficients of SM and GWS estimates by ~20% and 100%, respectively. The EvS outperforms the others, but its benefit is relatively marginal compared to Gaussian approaches (e.g., EnKS). This is due to the fact that SM and TWS error distributions in this study are close to Gaussian: a suitable condition for, e.g., EnKS, EnGPS. The robust performance of EvS appears to be the optimal approach for jointly assimilating multi-source hydrological observations to improve regional hydrological analyses

Reservoir-Induced Land Deformation: Case Study from the Grand Ethiopian Renaissance Dam

The Nile River stretches from south to north throughout the Nile River Basin (NRB) in Northeast Africa. Ethiopia, where the Blue Nile originates, has begun the construction of the Grand Ethiopian Renaissance Dam (GERD), which will be used to generate electricity. However, the impact of the GERD on land deformation caused by significant water relocation has not been rigorously considered in the scientific research. In this study, we develop a novel approach for predicting large-scale land deformation induced by the construction of the GERD reservoir. We also investigate the limitations of using the Gravity Recovery and Climate Experiment Follow On (GRACE-FO) mission to detect GERD-induced land deformation. We simulated three land deformation scenarios related to filling the expected reservoir volume, 70 km3, using 5-, 10-, and 15-year filling scenarios. The results indicated: (i) trends in downward vertical displacement estimated at −17.79 ± 0.02, −8.90 ± 0.09, and −5.94 ± 0.05 mm/year, for the 5-, 10-, and 15-year filling scenarios, respectively; (ii) the western (eastern) parts of the GERD reservoir are estimated to move toward the reservoir’s center by +0.98 ± 0.01 (−0.98 ± 0.01), +0.48 ± 0.00 (−0.48 ± 0.00), and +0.33 ± 0.00 (−0.33 ± 0.00) mm/year, under the 5-, 10- and 15-year filling strategies, respectively; (iii) the northern part of the GERD reservoir is moving southward by +1.28 ± 0.02, +0.64 ± 0.01, and +0.43 ± 0.00 mm/year, while the southern part is moving northward by −3.75 ± 0.04, −1.87 ± 0.02, and −1.25 ± 0.01 mm/year, during the three examined scenarios, respectively; and (iv) the GRACE-FO mission can only detect 15% of the large-scale land deformation produced by the GERD reservoir. Methods and results demonstrated in this study provide insights into possible impacts of reservoir impoundment on land surface deformation, which can be adopted into the GERD project or similar future dam construction plans

Global Climatologies of Vegetation Aerodynamic Roughness for Momentum: a fusion of MODIS and ICESat-2 Observations (d0s, DOY 001-177)

<p>This dataset includes the first half of the 8-day climatology fields (DOY 001-177) covering years 2003-2019 for the global land surface (90N - 60S) at 500-m spatial resolution as described in Borak et al. (2023). </p> <p>The d0s fields are formatted as 36000 lines by 86400 pixels of 32-bit floating-point binary data, with the first observation corresponding to the upper-left corner of the field. Projection is sinusoidal, and conforms to the same parameters as Collection 6+ 500-m MODIS products (radius of sphere = 6371007.181 m). Units of d0s are meters, with 255.0 referring to no-data.</p&gt

Global Climatologies of Vegetation Aerodynamic Roughness for Momentum: a fusion of MODIS and ICESat-2 Observations (static data fields)

<p>This dataset includes static z0s and d0s climatology fields for 2003-2019 for the global land surface (90N - 60S) at 500-m spatial resolution as described in Borak et al. (2023) as well as the long-term land cover map used to derive per-class information. </p> <p>The z0 and d0 fields are formatted as 36000 lines by 86400 pixels of 32-bit floating-point binary data, with the first observation corresponding to the upper-left corner of the field. Projection is sinusoidal, and conforms to the same parameters as Collection 6+ 500-m MODIS products (radius of sphere = 6371007.181 m). Units of z0 and d0 are meters, with 255.0 referring to no-data.</p> <p>The land cover field has identical consists of the long-term MODIS land cover classification (IGBP legend). Its spatial characteristics are identical to the roughness fields, but data are formatted as unsigned characters (i.e., 8-bit unsigned integers) with no-data and water set to 255.</p> <p>All files are compressed with gzip and packaged with tar.</p&gt

Global Climatologies of Vegetation Aerodynamic Roughness for Momentum: a fusion of MODIS and ICESat-2 Observations (d0s, DOY 185-361)

<p>This dataset includes the second half of the 8-day climatology fields (DOY 185-361) covering years 2003-2019 for the global land surface (90N - 60S) at 500-m spatial resolution as described in Borak et al. (2023). </p> <p>The d0s fields are formatted as 36000 lines by 86400 pixels of 32-bit floating-point binary data, with the first observation corresponding to the upper-left corner of the field. Projection is sinusoidal, and conforms to the same parameters as Collection 6+ 500-m MODIS products (radius of sphere = 6371007.181 m). Units of z0s are meters, with 255.0 referring to no-data.</p&gt

Global Climatologies of Vegetation Aerodynamic Roughness for Momentum: a fusion of MODIS and ICESat-2 Observations (z0s, DOY 185-361)

<p>This dataset includes the second half of the 8-day climatology fields (DOY 185-361) covering years 2003-2019 for the global land surface (90N - 60S) at 500-m spatial resolution as described in Borak et al. (2023). </p> <p>The z0s fields are formatted as 36000 lines by 86400 pixels of 32-bit floating-point binary data, with the first observation corresponding to the upper-left corner of the field. Projection is sinusoidal, and conforms to the same parameters as Collection 6+ 500-m MODIS products (radius of sphere = 6371007.181 m). Units of z0s are meters, with 255.0 referring to no-data.</p&gt

Global Climatologies of Vegetation Aerodynamic Roughness for Momentum: a fusion of MODIS and ICESat-2 Observations (z0s, DOY 001-177)

<p>This dataset includes the first half of the 8-day climatology fields (DOY 001-177) covering years 2003-2019 for the global land surface (90N - 60S) at 500-m spatial resolution as described in Borak et al. (2023). </p> <p>The z0s fields are formatted as 36000 lines by 86400 pixels of 32-bit floating-point binary data, with the first observation corresponding to the upper-left corner of the field. Projection is sinusoidal, and conforms to the same parameters as Collection 6+ 500-m MODIS products (radius of sphere = 6371007.181 m). Units of z0s are meters, with 255.0 referring to no-data.</p&gt