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Remote Sensing of Groundwater from River Basin to Global Scales

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (28 February 2018) | Viewed by 114424

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Special Issue Editors

Dr. Ehsan Forootan

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Guest Editor

School of Earth and Ocean Sciences, Cardiff University, Cardiff, UK
Interests: satellite gravity; satellite altimetry; signal processing; inversion; data assimilation
Special Issues, Collections and Topics in MDPI journals

Dr. Di Long

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Guest Editor

Tsinghua University, Beijing, China
Interests: hydrology; remote sensing; modeling; GRACE; evapotranspiration

Prof. Dr. Shin-Chan Han

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Guest Editor

School of Engineering, University of Newcastle, Callaghan, Australia
Interests: GNSS/GPS; geodesy; geophysics; planetary sciences

Prof. Dr. Ibrahim Hoteit

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Guest Editor

Climate Change Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Interests: ocean modelling; data assimilation; remote sensing; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Satellite-based remote sensing has become an emerging technique for monitoring the terrestrial water cycle from regional to global scales. Satellite geodesy methods such as Gravity Recovery And Climate Experience (GRACE), Global Positioning System (GPS), and satellite altimetry provide alternative ways of measuring water, ice, and snow distribution within the Earth system. Various components of the terrestrial water cycle have been measured by satellite-based sensors like Advanced Microwave Scanning Radiometer (AMSR), Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) for surface soil moisture; Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) for precipitation; Moderate Resolution Imaging Spectroradiometer (MODIS) for vegetation cover, to name a few.

Despite the ever increasing interest of using remote sensing technique, subsurface hydrology has yet to realize its benefits for monitoring groundwater from space. The goal of this Special Issue is to demonstrate the contribution of satellite observations and physical/conceptual/statistical modelling techniques to estimating groundwater storage changes and discharge from river basin to global scales. Contributions introducing the latest developments in terms of new sensors and satellite missions that will be available in the near future, as well as those addressing integration of remote sensing products with the surface and groundwater process models are particularly invited.

Dr. Ehsan Forootan
Dr. Di Long
Prof. Shin-Chan Han
Prof. Ibrahim Hoteit
Guest Editors

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Keywords

Remote Sensing
Satellite Geodesy
GRACE
Altimetry
Glacier and Snow Remote Sensing
Soil Moisture Remote Sensing
Groundwater storage and discharge
River Basin
Hydrological Model
Statistical Model
Conceptual Model
Data-Model Integration

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Published Papers (10 papers)

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20 pages, 4032 KiB

Open AccessEditor’s ChoiceArticle

Seasonal and Decadal Groundwater Changes in African Sedimentary Aquifers Estimated Using GRACE Products and LSMs

by H. C. Bonsor, M. Shamsudduha, B. P. Marchant, A. M. MacDonald and R. G. Taylor

Remote Sens. 2018, 10(6), 904; https://doi.org/10.3390/rs10060904 - 8 Jun 2018

Cited by 60 | Viewed by 8690

Abstract

Increased groundwater abstraction is important to the economic development of Africa and to achieving many of the Sustainable Development Goals. However, there is little information on long-term or seasonal groundwater trends due to a lack of in situ monitoring. Here, we used GRACE data from three products (the Centre for Space Research land solution (CSR), the Jet Propulsion Laboratory’s Global Mascon solution (JPL-MSCN), and the Centre National D’etudes Spatiales / Groupe de Recherches de Géodésie Spatiale solution (GRGS)), to examine terrestrial water storage (TWS) changes in 12 African sedimentary aquifers, to examine relationships between TWS and rainfall , and estimate groundwater storage (GWS) changes using four Land Surface Models (LSMs) (Community Land Model (CLM2.0), the Variable Infiltration Capacity model (VIC), the Mosaic model (MOSAIC) and the Noah model (NOAH)). We find that there are no substantial continuous long-term decreasing trends in groundwater storage from 2002 to 2016 in any of the African basins, however, consistent rising groundwater trends amounting to ~1 km³/year and 1.5 km³/year are identified in the Iullemmeden and Senegal basins, respectively, and longer term variations in ΔTWS in several basins associated with rainfall patterns. Discrete seasonal ΔTWS responses of ±1–5 cm/year are indicated by GRACE for each of the basins, with the exception of the Congo, North Kalahari, and Senegal basins, which display larger seasonal ΔTWS equivalent to approx. ±11–20 cm/year. The different seasonal responses in ΔTWS provide useful information about groundwater, including the identification of 5 to 9 month accumulation periods of rainfall in many semi-arid and arid basins as well as differences in ΔTWS responses between Sahelian and southern African aquifers to similar rainfall, likely reflecting differences in landcover. Seasonal ΔGWS estimated by combining GRACE ΔTWS with LSM outputs compare inconsistently to available in situ measurements of groundwater recharge from different basins, highlighting the need to further develop the representation of the recharge process in LSMs and the need for more in situ observations from piezometry. Full article

(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)

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17 pages, 2763 KiB

Open AccessArticle

What Is the Spatial Resolution of grace Satellite Products for Hydrology?

by Bramha Dutt Vishwakarma, Balaji Devaraju and Nico Sneeuw

Remote Sens. 2018, 10(6), 852; https://doi.org/10.3390/rs10060852 - 31 May 2018

Cited by 82 | Viewed by 13207

Abstract

The mass change information from the Gravity Recovery And Climate Experiment (grace) satellite mission is available in terms of noisy spherical harmonic coefficients truncated at a maximum degree (band-limited). Therefore, filtering is an inevitable step in post-processing of grace fields to extract meaningful information about mass redistribution in the Earth-system. It is well known from previous studies that a number can be allotted to the spatial resolution of a band-limited spherical harmonic spectrum and also to a filtered field. Furthermore, it is now a common practice to correct the filtered grace data for signal damage due to filtering (or convolution in the spatial domain). These correction methods resemble deconvolution, and, therefore, the spatial resolution of the corrected grace data have to be reconsidered. Therefore, the effective spatial resolution at which we can obtain mass changes from grace products is an area of debate. In this contribution, we assess the spatial resolution both theoretically and practically. We confirm that, theoretically, the smallest resolvable catchment is directly related to the band-limit of the spherical harmonic spectrum of the grace data. However, due to the approximate nature of the correction schemes and the noise present in grace data, practically, the complete band-limited signal cannot be retrieved. In this context, we perform a closed-loop simulation comparing four popular correction schemes over 255 catchments to demarcate the minimum size of the catchment whose signal can be efficiently recovered by the correction schemes. We show that the amount of closure error is inversely related to the size of the catchment area. We use this trade-off between the error and the catchment size for defining the potential spatial resolution of the grace product obtained from a correction method. The magnitude of the error and hence the spatial resolution are both dependent on the correction scheme. Currently, a catchment of the size ≈63,000 km

^{2}

can be resolved at an error level of

2 cm

in terms of equivalent water height. Full article

(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)

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16 pages, 2517 KiB

Open AccessArticle

Groundwater Depletion in the West Liaohe River Basin, China and Its Implications Revealed by GRACE and In Situ Measurements

by Yulong Zhong, Min Zhong, Wei Feng, Zizhan Zhang, Yingchun Shen and Dingcheng Wu

Remote Sens. 2018, 10(4), 493; https://doi.org/10.3390/rs10040493 - 21 Mar 2018

Cited by 94 | Viewed by 11447

Abstract

The West Liaohe River Basin (WLRB) is one of the most sensitive areas to climate change in China and an important grain production base in the Inner Mongolia Autonomous Region of China. Groundwater depletion in this region is becoming a critical issue. Here, we used the Gravity Recovery and Climate Experiment (GRACE) satellite data and in situ well observations to estimate groundwater storage (GWS) variations and discussed the driving factors of GWS changes in the WLRB. GRACE detects a GWS decline rate of −0.92 ± 0.49 km³/yr in the WLRB during 2005–2011, consistent with the estimate from in situ observations (−0.96 ± 0.19 km³/yr). This long-term GWS depletion is attributed to reduced precipitation and extensive groundwater overexploitation in the 2000s. Long-term groundwater level observations and reconstructed total water storage variations since 1980 show favorable agreement with precipitation anomalies at interannual timescales, both of which are significantly influenced by the El Niño-Southern Oscillation (ENSO). Generally, the WLRB receives more/less precipitation during the El Niño/La Niña periods. One of the strongest El Niño events on record in 1997–1998 and a subsequent strong La Niña drastically transform the climate of WLRB into a decade-long drought period, and accelerate the groundwater depletion in the WLRB after 1998. This study demonstrates the significance of integrating satellite observations, ground-based measurements, and climatological data for interpreting regional GWS changes from a long-term perspective. Full article

(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)

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26 pages, 17420 KiB

Open AccessArticle

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

by Natthachet Tangdamrongsub, Shin-Chan Han, Siyuan Tian, Hannes Müller Schmied, Edwin H. Sutanudjaja, Jiangjun Ran and Wei Feng

Remote Sens. 2018, 10(3), 483; https://doi.org/10.3390/rs10030483 - 20 Mar 2018

Cited by 52 | Viewed by 10449

Abstract

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. Full article

(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)

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18 pages, 1962 KiB

Open AccessArticle

Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California’s Central Valley

by Michelle E. Miro and James S. Famiglietti

Remote Sens. 2018, 10(1), 143; https://doi.org/10.3390/rs10010143 - 19 Jan 2018

Cited by 98 | Viewed by 12867

Abstract

NASA’s Gravity Recovery and Climate Experiment (GRACE) has already proven to be a powerful data source for regional groundwater assessments in many areas around the world. However, the applicability of GRACE data products to more localized studies and their utility to water management authorities have been constrained by their limited spatial resolution (~200,000 km²). Researchers have begun to address these shortcomings with data assimilation approaches that integrate GRACE-derived total water storage estimates into complex regional models, producing higher-resolution estimates of hydrologic variables (~2500 km²). Here we take those approaches one step further by developing an empirically based model capable of downscaling GRACE data to a high-resolution (~16 km²) dataset of groundwater storage changes over a portion of California’s Central Valley. The model utilizes an artificial neural network to generate a series of high-resolution maps of groundwater storage change from 2002 to 2010 using GRACE estimates of variations in total water storage and a series of widely available hydrologic variables (PRISM precipitation and temperature data, digital elevation model (DEM)-derived slope, and Natural Resources Conservation Service (NRCS) soil type). The neural network downscaling model is able to accurately reproduce local groundwater behavior, with acceptable Nash-Sutcliffe efficiency (NSE) values for calibration and validation (ranging from 0.2445 to 0.9577 and 0.0391 to 0.7511, respectively). Ultimately, the model generates maps of local groundwater storage change at a 100-fold higher resolution than GRACE gridded data products without the use of computationally intensive physical models. The model’s simulated maps have the potential for application to local groundwater management initiatives in the region. Full article

(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)

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25 pages, 18176 KiB

Open AccessArticle

Incorporation of Satellite Data and Uncertainty in a Nationwide Groundwater Recharge Model in New Zealand

by Rogier Westerhoff, Paul White and Zara Rawlinson

Remote Sens. 2018, 10(1), 58; https://doi.org/10.3390/rs10010058 - 3 Jan 2018

Cited by 11 | Viewed by 6078

Abstract

A nationwide model of groundwater recharge for New Zealand (NGRM), as described in this paper, demonstrated the benefits of satellite data and global models to improve the spatial definition of recharge and the estimation of recharge uncertainty. NGRM was inspired by the global-scale WaterGAP model but with the key development of rainfall recharge calculation on scales relevant to national- and catchment-scale studies (i.e., a 1 km × 1 km cell size and a monthly timestep in the period 2000–2014) provided by satellite data (i.e., MODIS-derived evapotranspiration, AET and vegetation) in combination with national datasets of rainfall, elevation, soil and geology. The resulting nationwide model calculates groundwater recharge estimates, including their uncertainty, consistent across the country, which makes the model unique compared to all other New Zealand estimates targeted towards groundwater recharge. At the national scale, NGRM estimated an average recharge of 2500 m

^{3}

/s, or 298 mm/year, with a model uncertainty of 17%. Those results were similar to the WaterGAP model, but the improved input data resulted in better spatial characteristics of recharge estimates. Multiple uncertainty analyses led to these main conclusions: the NGRM model could give valuable initial estimates in data-sparse areas, since it compared well to most ground-observed lysimeter data and local recharge models; and the nationwide input data of rainfall and geology caused the largest uncertainty in the model equation, which revealed that the satellite data could improve spatial characteristics without significantly increasing the uncertainty. Clearly the increasing volume and availability of large-scale satellite data is creating more opportunities for the application of national-scale models at the catchment, and smaller, scales. This should result in improved utility of these models including provision of initial estimates in data-sparse areas. Topics for future collaborative research associated with the NGRM model include: improvement of rainfall-runoff models, establishment of snowmelt and river recharge modules, further improvement of estimates of rainfall and AET, and satellite-derived AET in irrigated areas. Importantly, the quantification of uncertainty, which should be associated with all future models, should give further impetus to field measurements of rainfall recharge for the purpose of model calibration. Full article

(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)

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4986 KiB

Open AccessEditor’s ChoiceArticle

Using GRACE Satellite Gravimetry for Assessing Large-Scale Hydrologic Extremes

by Alexander Y. Sun, Bridget R. Scanlon, Amir AghaKouchak and Zizhan Zhang

Remote Sens. 2017, 9(12), 1287; https://doi.org/10.3390/rs9121287 - 11 Dec 2017

Cited by 37 | Viewed by 9387

Abstract

Global assessment of the spatiotemporal variability in terrestrial total water storage anomalies (TWSA) in response to hydrologic extremes is critical for water resources management. Using TWSA derived from the gravity recovery and climate experiment (GRACE) satellites, this study systematically assessed the skill of the TWSA-climatology (TC) approach and breakpoint (BP) detection method for identifying large-scale hydrologic extremes. The TC approach calculates standardized anomalies by using the mean and standard deviation of the GRACE TWSA corresponding to each month. In the BP detection method, the empirical mode decomposition (EMD) is first applied to identify the mean return period of TWSA extremes, and then a statistical procedure is used to identify the actual occurrence times of abrupt changes (i.e., BPs) in TWSA. Both detection methods were demonstrated on basin-averaged TWSA time series for the world’s 35 largest river basins. A nonlinear event coincidence analysis measure was applied to cross-examine abrupt changes detected by these methods with those detected by the Standardized Precipitation Index (SPI). Results show that our EMD-assisted BP procedure is a promising tool for identifying hydrologic extremes using GRACE TWSA data. Abrupt changes detected by the BP method coincide well with those of the SPI anomalies and with documented hydrologic extreme events. Event timings obtained by the TC method were ambiguous for a number of river basins studied, probably because the GRACE data length is too short to derive long-term climatology at this time. The BP approach demonstrates a robust wet-dry anomaly detection capability, which will be important for applications with the upcoming GRACE Follow-On mission. Full article

(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)

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28991 KiB

Open AccessArticle

Characterizing Drought and Flood Events over the Yangtze River Basin Using the HUST-Grace2016 Solution and Ancillary Data

by Hao Zhou, Zhicai Luo, Natthachet Tangdamrongsub, Lunche Wang, Lijie He, Chuang Xu and Qiong Li

Remote Sens. 2017, 9(11), 1100; https://doi.org/10.3390/rs9111100 - 27 Oct 2017

Cited by 38 | Viewed by 7386

Abstract

Accurate terrestrial water storage (TWS) estimation is important to evaluate the situation of the water resources over the Yangtze River Basin (YRB). This study exploits the TWS observation from the new temporal gravity field model, HUST-Grace2016 (Huazhong University of Science and Technology), which is developed by a new low-frequency noise processing strategy. A novel GRACE (Gravity Recovery and Climate Experiment) post-processing approach is proposed to enhance the quality of the TWS estimate, and the improved TWS is used to characterize the drought and flood events over the YRB. The HUST-Grace2016-derived TWS presents good agreement with the CSR (Center for Space Research) mascon solution as well as the PCR-GLOBWB (PCRaster Global Water Balance) hydrological model. Particularly, our solution provides remarkable performance in identifying the extreme climate events e.g., flood and drought over the YRB and its sub-basins. The comparison between GRACE-derived TWS variations and the MODIS-derived (Moderate Resolution Imaging Spectroradiometer) inundated area variations is then conducted. The analysis demonstrates that the terrestrial reflectance data can provide an alternative way of cross-comparing and validating TWS information in Poyang Lake and Dongting Lake, with a correlation coefficient of 0.77 and 0.70, respectively. In contrast, the correlation is only 0.10 for Tai Lake, indicating the limitation of cross-comparison between MODIS and GRACE data. In addition, for the first time, the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) vertical velocity data is incorporated with GRACE TWS in the exploration of the climate-induced hydrological activities. The good agreement between non-seasonal NCEP/NCAR vertical velocities and non-seasonal GRACE TWSs is found in flood years (2005, 2010, 2012 and 2016) and drought years (2006, 2011 and 2013). The evidence shown in this study may contribute to the analysis of the mechanism of climate impacts on the YRB. Full article

(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)

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25 pages, 3950 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Monitoring Groundwater Storage Changes Using the Gravity Recovery and Climate Experiment (GRACE) Satellite Mission: A Review

by Frédéric Frappart and Guillaume Ramillien

Remote Sens. 2018, 10(6), 829; https://doi.org/10.3390/rs10060829 - 25 May 2018

Cited by 205 | Viewed by 18779

Abstract

The Gravity Recovery and Climate Experiment (GRACE) satellite mission, which was in operation from March 2002 to June 2017, was the first remote sensing mission to provide temporal variations of Terrestrial Water Storage (TWS), which is the sum of the water masses that were contained in the soil column (i.e., snow, surface water, soil moisture, and groundwater), at a spatial resolution of a few hundred kilometers. As in situ level measurements are generally not sufficiently available for monitoring groundwater changes at the regional-scale, this unique dataset, combined with external information, is widely used to quantify the interannual variations of groundwater storage in the world’s major aquifers. GRACE-based groundwater changes revealed significant aquifer depletion over large regions, such as the Middle East, the northwest India aquifer, the North China Plain aquifer, the Murray-Darling Basin in Australia, the High Plains, and the California Central Valley aquifers in the United States of America (USA), but were also used to estimate groundwater-related parameters such as the specific yield, which relates groundwater level to storage, or to define the indices of groundwater depletion and stress. In this review, the approaches used for estimating groundwater storage variations are presented along with the main applications of GRACE data for groundwater monitoring. Issues that were related to the use of GRACE-based TWS are also addressed. Full article

(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)

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25 pages, 7279 KiB

Open AccessFeature PaperReview

Groundwater Storage Changes in China from Satellite Gravity: An Overview

by Wei Feng, C. K. Shum, Min Zhong and Yun Pan

Remote Sens. 2018, 10(5), 674; https://doi.org/10.3390/rs10050674 - 26 Apr 2018

Cited by 163 | Viewed by 14818

Abstract

Groundwater plays a critical role in the global water cycle and is the drinking source for almost half of the world’s population. However, exact quantification of its storage change remains elusive due primarily to limited ground observations in space and time. The Gravity Recovery and Climate Experiment (GRACE) twin-satellite data have provided global observations of water storage variations at monthly sampling for over a decade and a half, and is enable to estimate changes in groundwater storage (GWS) after removing other water storage components using auxiliary datasets and models. In this paper, we present an overview of GWS changes in three main aquifers within China using GRACE data, and conduct a comprehensive accuracy assessment using in situ ground well observations and hydrological models. GRACE detects a significant GWS depletion rate of 7.2 ± 1.1 km³/yr in the North China Plain (NCP) during 2002–2014, consistent with ground well observations and model predictions. The Liaohe River Basin (LRB) experienced a pronounced GWS decline during 2005–2009, at a depletion rate of 5.0 ± 1.2 km³/yr. Since 2010, GRACE-based GWS reveal a slow recovery in the LRB, with excellent agreement with ground well observations. For the whole study period 2002–2014, no significant long-term GWS depletion is found in the LRB nor in the Tarim Basin. A case study in the Inner Tibetan Plateau highlights there still exist large uncertainties in GRACE-based GWS change estimates. Full article

(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)

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