Monitoring land subsidence induced by groundwater change using satellite gravimetry and radar interferometry measurements. Case study: Surabaya city, Indonesia

Land subsidence is considered a potential hazard often occurring in densely populated urban areas due to increasing freshwater demands from groundwater pumping. The Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry combined with Sentinel 1 interferometric satellite radar measurement has provided the possibility to monitor land subsidence induced by groundwater change. This study monitored land subsidence induced by groundwater change through satellite observations over Surabaya City, Indonesia, from 2014 to 2019. Persistent Scattered InSAR (PSInSAR) measurement was used to monitor land subsidence by using 114 SLC pairs. As for the groundwater perspective, Global Land Data Assimilation System (GLDAS v.2.2), which contains the Groundwater Storage Anomaly (GWS) derived from GRACE satellite observation, was used to understand groundwater’s spatial and temporal variation. The results show a satisfactory agreement of satellite radar measurement with ground measurement (R = 0.96, RMSE = 4.92cm), while satellite gravimetry measurement showed reasonably good agreement with radar measurement as well (R = 0.25). Regarding the magnitude and occurrence of land subsidence over Surabaya City, the result shows that, over the past 5 years, the southern part of the city had the highest subsidence ranging from -10 mm/year to -40 mm/year. Therefore, the results conclude the capability of both satellite gravimetry and radar measurements to monitor land subsidence over an urban area. Thus, this information could be considered as an important decision-making process for disaster management purposes.

Geodetic observations for constraining mantle processes in Antarctica

Geodynamic processes in Antarctica such as glacial isostatic adjustment (GIA) and post-seismic deformation are measured by geodetic observations such as GNSS and satellite gravimetry. GNSS measurements have been comprising continuous measurements as well as episodic measurements since the mid-1990s. The estimated velocities typically reach an accuracy of 1 mm/a for horizontal and 2 mm/a for vertical velocities. However, the elastic deformation due to present-day ice-load change needs to be considered accordingly.Space gravimetry derives mass changes from small variations in the inter-satellite distance of a pair of satellites, starting with the GRACE satellite mission in 2002 and continuing with the GRACE-FO mission launched in 2018. The spatial resolution of the measurements is low (about 300 km) but the measurement error is homogeneous across Antarctica. The estimated trends contain signals from ice mass change, local and global GIA signal. To combine the strengths of the individual data sets statistical combinations of GNSS, GRACE and satellite altimetry data have been developed. These combinations rely on realistic error estimates and assumptions of snow density. Nevertheless, they capture signal that is missing from geodynamic forward models such as the large uplift in the Amundsen Sea sector due to low-viscous response to century-scale ice-mass changes.

Appraisal of Remote Sensing Technology for Groundwater Resource Management Perspective in Indus Basin

The dynamic nature and unsustainable exploitation of groundwater aquifers pose a range of management challenges. The accurate basin-wide hydrological assessment is very critical for the quantification of abstraction rates, spatial patterns of groundwater usage, recharge and discharge processes, and identification of critical areas having groundwater mining. This study provides the appraisal of remote sensing technology in comparison with traditionally prevailing tools and methodologies and introduces the practical use of remote sensing technology to bridge the data gaps. It demonstrates the example of Gravity Recovery and Climate Experiment (GRACE) satellite inferred Total Water Storage (TWS) information to quantify the behavior of the Upper Indus Plain Aquifer. The spatio-temporal changes in aquifer usage are investigated particularly for irrigation and anthropogenic purposes in general. The GRACE satellite is effective in capturing the water balance components. The basin-wide monthly scale groundwater storage monitoring is a big opportunity for groundwater managers and policymakers. The remote sensing integrated algorithms are useful tools to provide timely and valuable information on aquifer behavior. Such tools are potentially helpful to support the implementation of groundwater management strategies, especially in the developing world where data scarcity is a major challenge. Groundwater resources have not grown to meet the growing demands of the population, consequently, overexploitation of groundwater resources has occurred in these decades, leading to groundwater decline. However, future developments in the field of space technology are envisioned to overcome the currently faced spatio-temporal challenges.

A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm

Evapotranspiration (ET) is a vital part of the hydrological cycle and the water–energy balance. To explore the characteristics of five typical remote sensing evapotranspiration datasets and provide guidance for algorithm development, we used reconstructed evapotranspiration (Recon) data based on ground and GRACE satellite observations as a benchmark and evaluated five remote sensing datasets for 592 watersheds across the continental United States. The Global Land Evaporation Amsterdam Model (GLEAM) dataset (with bias and RMSE values of 23.18 mm/year and 106.10 mm/year, respectively), process-based land surface evapotranspiration/heat flux (P-LSH) dataset (bias = 22.94 mm/year and RMSE = 114.44 mm/year) and the Penman–Monteith–Leuning (PML) algorithm generated ET dataset (bias = −17.73 mm/year and RMSE = 108.97 mm/year) showed the better performance on a yearly scale, followed by the model tree ensemble (MTE) dataset (bias = 99.45 mm/year and RMSE = 141.32 mm/year) and the moderate-resolution imaging spectroradiometer (MODIS) dataset (bias = −106.71 mm/year and RMSE = 158.90 mm/year). The P-LSH dataset outperformed the other four ET datasets on a seasonal scale, especially from March to August. Both PML and MTE showed better overall accuracy and could accurately capture the spatial variability of evapotranspiration in arid regions. The P-LSH and GLEAM products were consistent with the Recon data in middle-value section. MODIS and MTE had larger bias and RMSE values on a yearly scale, whereby the MODIS and MTE datasets tended to underestimate and overestimate ET values in all the sections, respectively. In the future, the aim should be to reduce bias in the MODIS and MTE algorithms and further improve seasonality of the ET estimation in the GLEAM algorithm, while the estimation accuracy of the P-LSH and MODIS algorithms should be improved in arid regions. Our analysis suggests that combining artificial intelligence algorithms or data-driven algorithms and physical process algorithms will further improve the accuracy of ET estimation algorithms and the quality of ET datasets, as well as enhancing their capacity to be applied in different climate regions.

Iran Total Water Storage from May 2018 to 2020

Total water storage (TWS) is obtained by the gravity recovery and climate experiment (GRACE) satellite by measuring surface density changes. TWS provides valuable information about the water cycle on Earth and can play a useful role in water resource management studies. In this study, TWS of GRACE- Follow-On (FO) satellite from 2018 to 2020 has been studied. The western and central regions of Iran are in a better situation than other regions. Northwest, North, Northeast areas have a decreasing TWS trend of more than 5 cm. The western regions of Iran show an increasing trend and its value is about 3 cm. The southern regions of Iran have an almost constant annual TWS trend. TWS 2020 is similar to 2019.

Seasonal variability of the net water-mass transport among the four major basins

<p>Global water cycle involves water-mass transport on land, atmosphere, ocean, and among them. Quantification of such transport, and especially its time evolution, is essential to identify footprints of the climate change and helps to constrain and improve climatic models. In the ocean, net water-mass transport among the ocean basins is a key, but poorly estimated parameter presently. We propose a new methodology that incorporates the time-variable gravity observations from the GRACE satellite (2003-2016) to estimate the change of water content, and that overcomes some fundamental limitations of existing approaches. We show that the Pacific and Arctic Oceans receive an average of 1916 (95% confidence interval [1812, 2021]) Gt/month (~0.72 ± 0.02 Sv) of excess freshwater from the atmosphere and the continents that gets discharged into the Atlantic and Indian Oceans, where net evaporation minus precipitation returns the water to complete the cycle. This salty water-mass transport from the Pacific and Arctic Oceans to the Atlantic and Indian Oceans show a clear seasonal variability, with a maximum transport of 3000 Gt/month during boreal summer, a minimum of 1000 Gt/month or less on February, Mars, and November.</p><p>This research has been primarily supported by the Spanish Ministerio de Ciencia, Innovación and Universidades research project DEEP-MAPS (RTI2018-093874-B-I00).</p>