Water storage

C H A P T E R

20 Water storage O U T L I N E 20.1 Introduction

765

20.2 Water balanceebased estimation

766

20.3 Surface parameterebased estimation 767 20.3.1 Principles 767 20.3.2 Satellite-derived water surface area 768 20.3.2.1 Optical satellite sensors 768 20.3.2.2 Active microwave sensors 770 20.3.2.3 Passive microwave sensors 772 20.3.2.4 Combination of multisatellite sensors 772 20.3.3 Satellite-derived water level 773

20.3.3.1 Water level/area relationship method 20.3.3.2 Landewater contact method 20.3.3.3 Satellite altimetry method

20.3.4 Applications

773 773 774 777

20.4 GRACE-based estimation 20.4.1 GRACE satellite 20.4.2 Principles 20.4.3 GRACE dataset and applications

779 779 779 780

20.5 Summary

783

References

783

Abstract

20.1 Introduction

Monitoring water storage and its variation is important to understanding local hydrological processes and the global water cycle, which sustains all life on Earth. The development of satellite remote sensing techniques has benefited the retrieval of terrestrial water storage and its variation, which has emerged as a new discipline. Focusing on terrestrial water storage, this chapter describes major retrieval approaches: the water balanceebased approach, the surface parameterebased approach, and the Gravity Recovery and Climate Experimentebased approach. Accurate estimates of terrestrial water storage and its variation are still being developed.

Surface water (as rivers, lakes, reservoirs, wetlands, and inundated areas) represents less than 1% of the total amount of water on Earth. However, it is one of the most important components in the terrestrial water and is essential for both human beings and ecosystems (Frappart et al., 2005). Terrestrial surface water plays a major role in global climate variability and the hydrological cycles (Calmant et al., 2008). The volume of water storage is an important parameter in

Advanced Remote Sensing, Second Edition https://doi.org/10.1016/B978-0-12-815826-5.00020-9

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© 2020 Elsevier Inc. All rights reserved.

766

20. Water storage

monitoring the quantity of surface water resources. The surface water storage variations also affect their physical, chemical, and biological processes. Therefore, accurate and timely monitoring of water storage variations in surface water is essential for the effective management of water allocation, ecosystem services, and for a better understanding of the impact of climate change and human activities on the terrestrial water resources (Birkett, 1995). However, our knowledge of water storage variations on terrestrial land is yet insufficient (Alsdorf and Lettenmaier, 2003). The volume of surface water storage cannot be measured directly. The traditional approach to monitor surface water storage relies on in situ measurements of water levels combined with accurate bathymetric data (Furnans and Austin, 2008). However, availability of continuous in situ measurement has been limited in most regions of the Earth. This approach is also time-consuming, labor-intensive, and costly. Therefore, it is a challenge to provide reliable surface water volume information given the absence of continuous and public in situ hydrological measurements. In the past few decades, remote sensing techniques offer the great potentials to large-scale hydrological observations. It enables to retrieve hydrological variables and parameters from space. The retrievals include precipitation (Chapter 17), evapotranspiration (Chapter 18), soil moisture (Chapter 19), and water surface area and stage (Smith and Pavelsky, 2009). Especially, the Gravity Recovery and Climate Experiment (GRACE) satellite provides a novel way to estimate water storage at global scale (Alsdorf and Lettenmaier, 2003; Schmidt et al., 2008). These developments have benefited the retrieval of terrestrial water storage and its variations. It emerges as a new discipline in remote sensing applications. Focusing on terrestrial water storage, this chapter describes three major retrieval approaches. Section 20.2 introduces water balanceebased

approach using the hydrologic components retrieved from remote sensing. Section 20.3 depicts surface parameterebased approach, in which advantages of satellite retrieval and hydraulic functions are described. Section 20.4 reviews the GRACE instrument and the principle of retrieving water storage. The last section makes remarks on future prospects of this new field.

20.2 Water balanceebased estimation In general, the volume of water storage in lakes or reservoirs is dependent on the balance between water inputs and outputs (Fig. 20.1). The water inputs consist of direct precipitation, surface runoff of the tributaries, and seepage, whereas the water outputs include evaporation, groundwater outflow, and surface water discharge (Cr
etaux and Birkett, 2006). Therefore, the simple water balance equation for a lake or reservoir can be written as: dS ¼ P  ET  Qs  Qg þ ε dt

(20.1)

where S is total water storage in a lake or reservoir (m3), and t is time in hour. dS/dt is the change in lake or reservoir water volume over the time period dt. P is the precipitation over the surface of the lake or reservoir (m3 h1). ET is the actual evapotranspiration from the lake or reservoir (m3 h1). Qs and Qg is the surface runoff and groundwater runoff, respectively (m3 h1). ε represents the accumulated errors from all components and other anthropogenic factors such as human water use. Water storage is the residual of the four components on the right side of Eq. (20.1). Water balance analysis method is an established approach of assessing changes in water volume, which seems simple enough. However, to close the water balance equation, all components should be measured or estimated independently. In this respect, the accuracy of the estimated water storage depends on the accuracy

767

20.3 Surface parameterebased estimation

P ET high water

Δs Qs

low water

Qg

FIGURE 20.1

Conceptual picture of water balance over a lake.

of each components. At present, there remain significant uncertainties to estimate the precipitation and evapotranspiration by using remote sensing (Roads et al., 2003; Kutoba et al., 2009). The largest uncertainty generally comes from ET estimates due to the limited temporal sampling of qualified satellite images and the large uncertainties in existing retrieval approach (Chapter 18). The large uncertainties restrict this water balanceebased approach from practical applications. Whatever, the approach is simple in principle. It may become operational once the retrieval approaches of the relevant four components mature with desired accuracy.

area of interest. Here, we briefly introduce the principle of the approach and its applications.

20.3.1 Principles The volume of water surface storage cannot be measured by any single satellite sensor data directly. Instead, it can be inferred indirectly by estimating the satellite-derived surface extent and water level. The principle of this method is as follows: Z h Z A S¼ AðhÞdh ¼ hðAÞdA (20.2) 0

20.3 Surface parameterebased estimation With the improved remote sensing techniques, numerous surface parameters including surface area and water level can be retrieved from satellite data. Given the parameter retrievals, water storage can be easily estimated with a digital elevation model (DEM) of the

0

where S is the volume of water storage (m3), h is the water level of terrestrial water bodies (m), and A is the surface water area (m2). A and h can be a function of the other. A straight forward approach to estimating S is to measure A and/or h. If the DEM is known, S can be estimated from either A or h. This method provides accurate volume estimations when detailed topographic data are available (Fig. 20.2).

768

20. Water storage

et al., 1973). With the development of remote sensing technology, it has become necessary to delineate the water surface extent from Earth observing satellites. At present, a variety of satellite sensor data are available for water surface area delineation at a range of spatial and temporal resolutions (Birkett, 2000; Alsdorf and Lettenmaier, 2003). The typical satellite instruments include FIGURE 20.2 Principle diagram of A-H method to calculate water storage.

In practical situation, topographic data are usually nonexistent or unavailable for given surface water bodies. Therefore, it is generally difficult to determine the absolute water volume of a lake or reservoir from space (Cretaux et al., 2005; Song et al., 2014). However, the determination of absolute volume of water storage is not fundamental when it is easier to calculate water volume variations. Instead, water storage variations dS, rather than S itself, becomes the central variables in the current studies (Cretaux et al., 2005). To estimate this variable, measurements of both surface water extent (A) and water level (h) at different dates are needed. Through multitemporal satellite data, water volume variations can be measured directly by estimating the variations of the surface extent and water level. A can be determined from VIS/IR/PMW satellite data (Jain et al., 2005) and h can be obtained from satellite radar altimetry. Then, a relationship between A and h may be established empirically. The empirical relationship allows dS to be retrievable from either A or h. Without the relationship, multitemporal observations of A or h can also be multiplied to produce dS. Satellite approaches for monitoring water extent (A) and water level (h) are described in detail below.

20.3.2 Satellite-derived water surface area Satellite retrieval algorithms of water surface area have long-time been developed (Hallberg

(1) Optical imagery, such as National Oceanic and Atmospheric Administration (NOAA), Advanced Very HigheResolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), Satellite pour 1’Observation de la Terre (SPOT), and Landsat Thematic Mapper (TM)/Enhanced Thematic Mapperþ; (2) Active microwave imagery, such as RADARSAT, Advanced Synthetic Aperture Radar (ASAR), Phased Array Type L-Band SAR (PALSAR), and European Space Agency (ERS) scatterometer. (3) Passive microwave imagery, such as Special Sensor Microwave/Imager (SSM/I), Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and TRMM Microwave Imager. Some of these sensors are listed in Table 20.1 for quick reference. Based on these satellite data, many accepted methods for the delineation of surface water extent have been proposed. According to different satellite sensor characteristics, these methods can be categorized into four basic types as follows. 20.3.2.1 Optical satellite sensors Optical satellite sensors have most frequently been employed in surface water extent delineation research (Huang et al., 2018). The parts of the electromagnetic spectrum covered by these sensors include the visible blue, green, and red wavelengths, as well as emissivity data through infrared wavelengths. In general, in contrast to bare soil and vegetation, water body is highly

769

20.3 Surface parameterebased estimation

TABLE 20.1

Overview of main satellite sensors for water surface delineation.

Sensor type

Satellite mission

Operation period

Spatial resolution

Temporal resolution

Optical satellite sensors

NOAA/AVHRR

1978epresent

1100 m

0.5 days

MODIS

1999epresent

250 m

0.5 days

Sentinel-3 OLCI

2016epresent

300 m

2 days

Landsat

1972epresent

30 m

16 days

SPOT

1986epresent

2.5e20 m

26 days

ASTER

1999epresent

15e90 m

16 day

HJ_1A/1B CCD

2008epresent

30 m

4 days

ENVISAT ASAR

2002e2012

30e1000m

35 days

RADARSAT

1995epresent

50e100 m

24 days

ERS

1991e2011

25 m

35 days

ALOS PALSAR

2006e2012

10e100m

46 days

TerraSAR-X

2007epresent

1e16 m

11 days

TANDEM-X

2010epresent

3m

11 days

1987epresent

25 km

1e2 days

2002epresent

25 km

1e2 days

1997e2014

25 km

1e2 days

Active microwave sensors

Passive microwave DMSP SSM/I sensors AQUA AMSR-E TRMM TMI

absorptive in the infrared bands and slightly more reflective in the visible bands (Fig. 20.3). According to this principle, in the past 40 years, a number of approaches have been proposed to

FIGURE 20.3

Main algorithm Single band method; Multiband index; Image classification; Decision tree method; Neural network method

Visual interpretation; Image classification; Histogram thresholding; Image texture analysis; Multitemporal change detection methods

Brightness temperature differences method

derive water surface area from optical satellite imagery (Alsdorf and Lettenmaier, 2003; Gao, 2015). The typical ones include single-band approach (Hallberg et al., 1973; Smith and

Reflectance characteristics comparison of water, vegetation, and soil.

770

20. Water storage

Pavelsky, 2009), supervised, and unsupervised image classification approach (Davranche et al., 2010; Jin et al., 2017; Berhane et al., 2018), decision tree approach (Acharya et al., 2016; Olthof, 2017), artificial neural network approach (Jiang et al., 2018), density slicing approach (Bennett, 1987; Frazier and Page, 2000; Jain et al., 2005), spectral analysis, and multiband water index approach (Hui et al., 2008; Jain et al., 2005; Tulbure et al., 2016).The multiband water index is generally derived from an arithmetic operation (e.g., ratio, difference, and normalized difference) of two or more spectral bands, such as normalized difference vegetation index (NDVI) (Ji et al., 2009), normalized difference water index (NDWI) (McFeeters, 1996), and modified normalized difference water index (Xu, 2006). These indices are described as follows: r  rRED NDVI ¼ NIR (20.3) rNIR þ rRED r  rNIR (20.4) NDWI ¼ GREEN rGREEN þ rNIR r  rMIR (20.5) MNDWI ¼ GREEN rGREEN þ rMIR where rNIR, rRED, rGREEN and rMIR indicate the reflectance values in the near-infrared, red, green, and mid-infrared bands of remote sensing imagery, respectively. From the index histogram, surface water bodies can be separated from their surrounding land cover types with a simple segmentation algorithm based on selected optimal threshold (Otsu, 1979). According to these methods, a considerable amount of research has been conducted on the surface water delineation in the past decade (Huang et al., 2018). The multiband water index method has been demonstrated to be more efficient and much more commonly used in various lakes, rivers, and inundated area (Jain et al., 2005; Hui et al., 2008; Wu and Liu, 2015b). The extracted results could be as accurate as up to 90% (Birkett, 2000). For example, Duane Nellis et al. (1998) used Landsat TM images to observe temporal and spatial variations in Tuttle Creek

Reservoir in Manhattan. Lu et al. (2011) evaluated the potential of the HJ-1A/B imagery on water body monitoring and proposed an integrated water mapping method (NDVI-NDWI index). Rokni et al. (2014) modeled the spatiotemporal changes of Lake Urmia in the period 2000e13 using the multitemporal Landsat imagery. More recently, based on MODIS images from 2000 to 2011, Wu and Liu (2015b) investigated the spatialetemporal distribution and changing processes of surface inundation in the Poyang Lake (the largest freshwater lake in China). Fig. 20.4 shows the Poyang Lake’s intraannual and interannual variations in average inundation area. The results were retrieved from MODIS reflective bands using the NDWI approach. It disclosed the large fluctuations of the lake at seasonal scale (Fig. 20.4A) and the decreasing trend of lake surface at annual scale in the last 10 years (Fig. 20.4B). 20.3.2.2 Active microwave sensors Retrievals of water surface area have been made successfully with optical sensors, but their routine applications are of limitation in that the optical imagery can be easily affected by clouds, smoke, floating emergent vegetation, and inundated forests (Alsdorf and Lettenmaier, 2003). Radar remote sensing offers an opportunity to routinely acquire surface water extent. It can penetrate cloud cover and can acquire data during day and night (Schumann and Moller, 2015). Synthetic aperture radar (SAR) is an advanced active microwave imagery system that emits microwave pulses at an oblique angle toward the target (Grimaldi et al., 2016). Examples include Japanese Earth Resources Satellite (JERS)-1 SAR, RADARSAT, ASAR, PALSAR, and so on. As a specular reflector, open water has a relatively smooth surface. Microwave energy is reflected away from the sensor, resulting in low backscatter in the SAR imagery (Bates et al., 2013). However, terrestrial land surfaces reflect the energy in many directions and generally appear as high backscatter features. Such

20.3 Surface parameterebased estimation

771

FIGURE 20.4 Satellite-retrieved seasonal (A) and annual (B) variations of water surface areas for Poyang Lake, the largest fresh lake in China.

differences make inundation extent easy to be delineated using many methods, and commonly used methods include simple visual interpretation, change vector analysis, supervised classification, image histogram thresholding, image texture algorithms and various multitemporal change detection methods (Schumann and Moller, 2015; Giustarini et al., 2016). Recent scientific literature is full of articles describing methodologies using SAR imagery for surface water extent mapping. For example, Wang (2004) studied seasonal change in inundation extent of North Carolina and South Carolina, USA, with JERS-1 SAR data by using a decision tree classification method. Voormansik et al. (2014) developed a supervised classification algorithm to produce high-resolution maps of

the flooded area from the TerraSAR-X images in forested regions in Estonia. Eilander et al. (2014) proposed a new Bayesian approach to delineate surface areas of small reservoirs in Ghana using the SAR imagery. Matgen et al. (2011) introduced an automated hybrid methodology for SAR-based water extent mapping that combines thresholding, region growing, and change detection, benefitting from the respective strengths of the specific processes. More recently, Pradhan et al. (2016) proposed an efficient methodology that is based on rule-based classification to recognize and map flooded areas by using TerraSAR-X imagery. However, active microwave remote sensing has its own deficiency. For example, it can only provide a limited number of images available per year in some

772

20. Water storage

regions, making the technique unsuitable for monitoring inundation variations in large water bodies (Gao, 2015; Pham-Duc et al., 2017). In addition, SAR-based water surface areas are likely to be confused by wind roughening of water surface or emergent vegetation, and the image processing is complicated (Smith and Alsdorf, 1998). 20.3.2.3 Passive microwave sensors Similar to active microwave measurements, passive microwave imagery can also reveal the presence of water surface despite cloud cover. In addition, passive microwave sensors can offer the advantage of higher revisit frequency of the available data collection (Hamilton et al., 2002; De Groeve, 2010).Theoretically, because of the different thermal inertia and dielectric properties of terrestrial land and water, the observed microwave radiation has a much lower brightness temperature for water bodies than for other land features (Grimaldi et al., 2016). Based on this principle, water surface extent can be efficiently detected, and detailed techniques have been proposed by many researchers. To estimate the water surface extent, Basist et al. (1998) proposed a Basin Wetness Index (BWI) based on the correlation between the decrease of emissivity and the brightness temperature differences. Sippe et al. (1998) determined inundation variations for the Amazon floodplain in Brazil based on an analysis of the 37 GHz polarization difference observed by the scanning multichannel microwave radiometer. Hamilton et al. (2002) applied a similar method to the South America. Likewise, Temimi et al. (2005)investigated the flood inundation area over the Mackenzie River Basin based on the use of a water surface fraction derived from SSM/I passive microwave images. However, the spatial resolutions of passive microwave imagery are very coarse (w25 km) due to the large angular beams of such systems. Therefore, the potential of using passive microwave imagery for water surface mapping is thus limited to very large scale (Bates et al., 2013). In addition, the quantitative estimates of subpixel inundation extent is very difficult

particularly when the flooded area is vegetated (Prigent et al., 2001; Papa et al., 2006). 20.3.2.4 Combination of multisatellite sensors In summary, surface water area and extent can be measured with a variety of satellite sensors, but each of which has its strengths and weaknesses. Some of the high spatial resolution optical sensors make it possible to accurately detect and delineate the water body information (Smith, 1997). However, the routine water monitoring with high spatial resolution optical data is difficult due to narrow scanning coverage and the long return period between successive satellites overpasses (Alsdorf and Lettenmaier, 2003). High temporal resolution multispectral data including MODIS and AVHRR have therefore been widely used to conduct routine inundation monitoring in mesoscale (Brakenridge and Anderson, 2006), but the resolution is relatively coarse. The overall uncertainty of these measurements is w6%e13% for small water bodies (Bryant and Rainey, 2002), which identified coarse spatial resolution imagery’s inability to detect small inundated regions. In addition, active microwave remote sensing data such as SAR can penetrate clouds and thick forest canopies but perform poorly on water with wind waves or roughened surfaces from emergent vegetation. Passive microwave observations have too coarse spatial resolutions, which restrict their potential values in regional applications (Temimi et al., 2007). To overcome the disadvantages of each satellite sensor, the methodology of combing the observations from multisource remote sensing (e.g., optical, active microwave, and passive microwave sensor data) to measure the water surface extent was gradually being recognized in recent years. For example, Townsend and Walsh (1998) combined SAR with Lansat TM imagery and a DEM to derive potential areas of inundation within the lower Roanoke River floodplain, North Carolina. T€ oyr€a et al. (2002) evaluated the use of radar (RADARSAT) and visible/infrared satellite imagery (SPOT) for mapping the extent of flooded wetland areas. Prigentet al. (2001,

20.3 Surface parameterebased estimation

2007) developed global floodplain and wetland inundation extent datasets with a suite of satellite observations, including passive and active microwave along with visible and infrared measurements. Likewise, Adhikari et al. (2010) used 250m resolution MODIS and other sensor data to map flooding in near real time and from this compile an archive of global floods from 1998 to 2008.

20.3.3 Satellite-derived water level There are several accepted methods for retrieving water levels using various remote sensing data. These methods can be summarized in three categories: the method of water level/ area relationship, landewater contact method based on DEM data, and radar altimetry method. 20.3.3.1 Water level/area relationship method Establishing the water level/area relationship is a simple approach that uses the water level/ area relationship to estimate the water level. For this method, a number of satellite images of inundation areas are needed to establish empirical rating curve relating water level to surface water area (Smith, 1997). This method was more commonly applied because water surface area is relatively easily to measure directly from satellite sensors with a high accuracy. For example, AI-Khudhairy et al. (2001) used multitemporal Landsat TM imagery and simultaneous groundbased measurements of water levels to establish statistical relationships between water area and water level and estimated historical water levels in the North Kent Marshes. Pan and Nichols (2013) successfully obtained 16 lake levels of Lake Champlain in Vermont through the constructed inundation area/lake level rating curves from satellite-measured inundation areas. However, to estimate lake level based on satellite measured inundation area, we first need the inundation areaelake level rating curve. The problem associated with this approach, as pointed out by Smith (1997), is that we often do

773

not have observed data to construct the inundation areaelake level curves. In addition, the derived relationships between water level and area are essentially empirical, and the transferability from one hydrogeomorphological setting to another has not been proven (Wu and Liu, 2015a). 20.3.3.2 Landewater contact method Water level can also be estimated using the landewater contact method, which uses water inundation extent in combination with highresolution topographic maps to derive water levels (Smith, 1997; Matgen et al., 2007; Schumann et al., 2008). This principle relies on the combination of the position of the watereland boundaries and a database of bottom topographic points (Fig. 20.5). Each water surface has contact line with the land at a corresponding water level. Thus, the challenge is to assign bottom topographic values to the pixels corresponding to the border of the water extent. The process can be described as follows. First, the water body from satellite images was transformed into water polygons. Next, the water polygons were used to generate vertices as points form the outer edge of watereland boundary pixel. Then, the height values along those watereland borders were extracted from the matched bottom topographic data. Finally, the spatial distribution of water level can then be constructed with the spatial interpolation method from the height values of the watereland borders. Several studies have attempted to monitor water level variation by the landewater contact method. For example, Raclot (2006) used this method to extract water levels from aerial photographs on floodplains, which have been shown to be sufficiently precise (mean  15 cm). Matgen et al. (2007) retrieved water level maps combining SAR-derived inundation areas with high-precision topographic data, and a root mean squared error of 41 cm was obtained. More recently, Wu and Liu (2015a) illustrated the use of MODIS images combined with topographic data to characterize complex water level

774

20. Water storage

FIGURE 20.5

Principle diagram of landewater contact method to derive water level.

variation in Poyang Lake. An error analysis was conducted to assess the derived water level relative to gauge data. Validation results demonstrated that this method can capture spatial patterns and seasonal variations in water level fluctuations. However, the absolute accuracy of the resulting map depends too much on DEM uncertainties and errors both in the horizontal and vertical directions (Zwenzner and Voigt, 2008). 20.3.3.3 Satellite altimetry method Satellite radar/laser altimetry is a promising technique for directly detecting water levels of open water bodies from space (Frappart et al., 2006). Satellite altimeters transmit a series of pulses toward the terrestrial surface in the nadir direction and receive the echo reflected by the surface (Duan and Bastiaanssen, 2013).The twoway travel time of radar (or laser) is measured and used to calculate the distance between the satellite and the target surface, called “range”. The shape of the reflected signal, known as the “waveform,” represents the power distribution of accumulated echoes as the radar pulse hits

the surface (Calmant et al., 2008).The principle of satellite altimetry is shown in Fig. 20.6. The water surface height can then be determined by the difference between the altitude of satellite orbit and the range observation, which is based on the following equation: H ¼ Alt  R  TE

(20.6)

where “Alt” is the satellite elevation above a reference ellipsoid provided by a precise orbit solution. “R” is the measured range. “TE” indicates various instrument and geophysical corrections, including atmospheric refraction, tidal effects, and so on (Birkett, 1995; Fu and Cazenave, 2011). At present, several altimetry satellites have been launched since the early 1990s. The most commonly used altimeters for measuring the height of terrestrial water bodies are Topex/ Poseidon (T/P) (1992e2002), ERS-1 (1992e2005), ERS-2 (1995e2003), Envisat (2002e12), Jason-1 (2002e08), Jason-2 (2008epresent), CryoSat-2 (2010epresent), SARAL (2013epresent), and Sentinel-3 (2015epresent). Different satellite altimetry sensors are flying at different orbits,

775

20.3 Surface parameterebased estimation

GPS SATELLITE

SATELLITE ORBIT

DORIS BEACON

LASER RANGING STATION

SEA SURFACE GEOID

OCEAN TOPOGRAPHY

SEA-FLOOR TOPOGRAPHY

NCE

REFFERE

FIGURE 20.6

SEA LAVEL

ID ELLIPSO

Principle of satellite altimetry. Source: http://oceantopo.jpl.nasa.gov.

resulting in the different spatiotemporal coverage of the surface water bodies. For example, T/P altimeter has a 10-days orbital cycle (temporal resolution) and 350 km intertrack spacing at the equator, while for CryoSat-2, the revisit cycle is 369 days (with the subcycle of 28 days) allowing a very dense coverage of the terrestrial surface. Table 20.2 provides a summary of the main instrument characteristics for past, current, and future satellite altimeter missions. So far, these satellite altimeter data have been widely used to monitor water levels in rivers (e.g., Tourian et al., 2016; Biancamaria et al., 2017; Pham et al., 2018; Huang et al., 2018), lakes (e.g., Cr
etaux and Birkett, 2006; Cr
etaux et al., 2015; Song et al., 2015; Jiang et al., 2017), reservoirs (e.g., Birkett and Beckley, 2010; Troitskaya et al., 2012; Duan and Bastiaanssen, 2013; Avisse et al., 2017), and floodplains (e.g., Dettmering et al., 2016; Yuan et al., 2017; Ovando et al., 2018). The altimetry technique has shown great potential in land surface hydrology research as these data are freely available worldwide, and it

is the essential information source for most water bodies in remote regions (Cr
etaux and Birkett, 2006). Fig. 20.7 shows the T/P altimeterederived relative stage variations of the Amazon River near the Manaus (Birkett et al., 2002). The T/P measurements (triangles) agreed quite well with in situ observations (solid line). However, there are also few limitations for monitoring some smaller inland water bodies (Cr
etaux et al., 2015). For one thing, waveform over inland water bodies are generally contaminated by land features, resulting in degraded accuracy or altimeter losing lock. To address this issue, the processing of current satellite altimetry waveform for inland water bodies still remains challenging (Liu et al., 2016). For another, each satellite altimeter is still limited by its long revisit periods (10e35 days) and coarser intertrack spatial resolution (Table 20.2). This limitation causes temporal or spatial gaps during each altimeter overpass (Cr
etaux et al., 2015; Biancamaria et al., 2017). To overcome this problem, it is expected to be continuously

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20. Water storage

TABLE 20.2

Overview of main satellite sensors for water surface delineation.

Altimeter sensor

Frequency

Operation period

Spatial resolution

Temporal resolution

ERS-1

Ku

1991e1995

80 km

35 days

TOPEX/Poseidon

Ku

1992e2005

315 km

10 days

ERS-2

Ku

1995e2003

80 km

35 days

Jason-1

Ku

2001e2013

315 km

10 days

Envisat

Ku

2002e2012

70 km

35 days

ICESat-1

Laser

2003e2010

170 m

91 days

Jason-2

Ku

2008epresent

315 km

10 days

CryoSat-2

Ku

2010epresent

7.5 km

369 days (subcycle: 28 days)

HY-2A

Ku

2011epresent

315 km

14 days

SARAL/AltiKa

Ka

2013epresent

80 km

35 days

Sentinel-3

Ku

2016epresent

104 km

27 days

Jason-3

Ku

2016epresent

315 km

10 days

ICESat-2

Laser

2017epresent

170 m

91 days

Jason-CS

Ku

Launch 2020

315 km

10 days

SWOT

Ka

Launch 2021

120 km wide swath with a 10 km gap

15e25 days

FIGURE 20.7

Relative stage changes of the Amazon River near Manaus. Courtesy of Birkett et al., 2002.

777

20.3 Surface parameterebased estimation

updated in the next few years with missions such as Jason-CS and SWOT (Surface Water Ocean Topography) (see Table 20.2), which will provide a high-resolution water surface height measurements for lakes and rivers as narrow as 100m with accuracy of 10 cm (Sulistioadi, 2013). In addition, combinations of multisatellite altimetry dataset are likely to be a better way to increase the spatiotemporal resolution of the derived water level (Calmant et al., 2008). In recent years, the combination from different altimeters for better spatiotemporal sampling of water level has been investigated by many researchers (Frappart et al., 2006; Birkett et al., 2011; Schwatke et al., 2015). Some global surface water level databases have also been developed by combining multisatellite altimeter observations. At present, three main global lakes’ and rivers’ water level databases based on different altimetry are operationally accessible. They are Global Reservoir and Lake Monitoring (GRLM) database by the US Department of Agriculture (USDA), River Lake Hydrology (RLH) database by European Space Agency (ESA), and Hydroweb database by the French Space Agency Center National d’Etudes Spatiales’ (CNES), which are listed in Table 20.3. Fig. 20.8 shows the global distribution of the lakes and reservoirs from Hydroweb database. For almost 150 lakes and reservoirs, monthly level variations can be freely provided by this database (Cr
etaux et al., 2011). This database is based on merged T/P, Jason, ERS-2, Envisat, and Geosat. Follow-on data are provided by ESA, the National Aeronautics and Space Administration (NASA), and CNES data centers. In a longer perspective, the Hydroweb database will integrate data from future missions (Jason-3, Jason-CS, Sentinel-3A/B) and finally will serve for the design of the SWOT mission.

20.3.4 Applications Multisatellite retrievals of water surface area and water levels from either an altimetry altimeter or in situ data are available to generate

TABLE 20.3

Database

List of global reservoir and lake level databases based on multialtimeter data.

Altimeter data source Period

URL

GRLM

T/P, Jason-1/2, Envisat

1992e present

http://www.pecad. fas.usda.gov/ cropexplorer/ global_reservoir/

RLH

Envisat, Jason-2

2002e present

http://tethys.eaprs. cse.dmu.ac.uk/ RiverLake/shared/ main

1992e present

http://www.legos. obs-mip.fr/soa/ hydrologie/ hydroweb/

Hydroweb T/P, Jason-1/2, 2ERS-2, GFO, Envisat

water storage variations. At present, many researchers have combined these two parameters to calculate lake and reservoir water storage changes worldwide (Gao et al., 2012; Song et al., 2013; Zhang et al., 2014; Cr
etaux et al., 2016). For example, Smith and Pavelsky (2009) calculated water storage variations in nine lakes of the PeacheAthabasca Delta, Canada, by using in situ water levels and satellite-derived surface areas. Duan and Bastiaanssen (2013) estimated water volume variations for three lakes and reservoirs by using four operational satellite altimetry databases combined with satellite imagery dada. Besides lakes and reservoirs, several studies also performed to reconstruct seasonal or interannual variations of water storage at large floodplains, such as Mekong River Basin and the Negro River Basin (Frappart et al., 2005, 2006, 2008). The Negro River Basin is the tributary carrying the largest discharge to the Amazon River. Fig. 20.9 displays the monthly variations of water storage in the basin estimated. It used water stage data from the T/P radar altimetry and inundation areas derived from multisatellite for the 1993e2000 periods (black line).The monthly variation from GRACE satellite was included for comparison, and the data were

778

20. Water storage

FIGURE 20.8

Schema of use of Hydroweb database.

FIGURE 20.9 Monthly water storage variations in the Negro River Basin estimated from Topex/Poseidon (T/P) altimetry and multisatelliteederived inundation areas over 1993e2000 period (black line) and monthly variation from GRACE averaged over 2003e05 (black dotted line).

779

20.4 GRACE-based estimation

averaged over 2003e05 (black dotted line). The overall results showed good agreement. It has been recognized that water storage accommodated lateral area variability or vertical stage adjustment varied significantly from one to other. Remote sensing estimates based on “area only” or “stage only” may lose important information of water storage variations. For example, inferring just from satellite-based areas may not work well in environments where small changes yield little surface area change but yield great variations in water storage (Alsdorf and Lettenmaier, 2003). A and h should be measured simultaneously for reliable estimation of storage variations. Recent work has demonstrated that Interferometric Synthetic Aperture Radar (InSAR) provides an intrinsic, image-based direct measurement to yield dh/dt (Alsdorf et al., 2000; Smith, 2002), and the accuracy could be up to centimeter scale. InSAR uses the phase values from two radar images (Smith, 2002). For water is highly reflective, microwave pulses from off-nadir imaging SAR reflect away from the antennae unless intercepted by vegetation. In this way, subtle changes of water level can be mapped. With water surface area determined from the radar imagery, water storage variations were then estimated from the simultaneous measurement.

20.4 GRACE-based estimation In addition to the water balanceebased and the surface parameterebased approaches, water storage can also be estimated with the data from the GRACE satellite (e.g., Schmidt et al., 2008). This approach is novel and of great potential in monitoring global water storage. We hereby introduce the GRACE satellite, the principle of the GRACE-based approach, the GRACE dataset, and its application.

20.4.1 GRACE satellite GRACE satellite mission, launched in March 17, 2002, from Plesetsk, Russia, was implemented

through a collaborative endeavor by NASA in the United States and Deutsche Forschungsanstalt f€ ur Luft- und Raumfahrt in Germany (Tapley and Reigber, 2002).With twin co-orbiting satellites at a low and near polar orbit, separated by a mean intersatellite distance of approximately 220 km, they measure continuous variations in the ranges of relative motion. Exploiting the differential observation, the underlying gravity field can be derived with high resolution (Fig. 20.10). It was originally designed to operate for 5 years and terminated in October 2017. It was expected to provide the Earth’s gravity with a ground resolution of 300e400 km, on an order of 150s for 1 month and 160s for the whole 5 years (Tapley et al., 2004).Fig. 20.10 shows the GRACE flight configuration, which involves science goals, deputy teams of mission systems, and orbit parameters.

20.4.2 Principles Temporal variations of the Earth’s gravity mainly result from surface redistribution of the water inside and among outer fluid envelopes of the Earth (including oceans, atmosphere, cryosphere, and hydrosphere). The GRACE provides the measures of vertically integrated total water storage change. Over the continents, it involves in river basins, surface reservoirs, soil moisture, and groundwater (Longuevergne et al., 2010). The change in the total hydrological signature from GRACE is often written as a combination of different components (Moore and Williams, 2014), namely DTWS ¼ DSWE þ DSME þ DSWS þ DCWE þ DGWS (20.7) where DTWS is the change in total water storage, DSWE is the ice and snow storage water equivalent, DSME is the soil moisture equivalent, DSWS is the surface water storage, DCWE is the water equivalent in the canopy, and DGWS is the groundwater storage. Therefore, the surface water storage (DSWS) can be investigated if the

780

20. Water storage

FIGURE 20.10 Flight configuration of GRACE. From http://www2.csr.utexas.edu/grace/mission/, Used with permission from The University of Texas Center for Space Research.

other components in Eq. (20.7) are negligible or estimated through land models or other remote sensing data. GRACE measures spatiotemporal change of the Earth’s gravity field at monthly interval. These monthly gravity fields are provided as sets of spherical harmonic coefficients, which can be inverted to global and regional estimates of vertically integrated total water storage change (DTWS) at a spatial resolution of few hundred kilometers or more. Based on the changes of spherical harmonic coefficients, we can estimate the water storage variations over a certain region (Wahr et al., 1998): Dsðq; lÞ ¼

N X n ara X 2n þ 1  3 n ¼ 0 m ¼ 0 1 þ kn0

where Dsðq; lÞ denotes the changes in surface areal density. q and l denote spherical coordinates with respect to an Earth-fixed frame. ra ¼ 5517 kg m3, which is the average density of the Earth. DCnm and DSnm are the corresponding changes of the harmonic coefficients with an order of m and degree of n. Pnm ðcos qÞ are fully normalized Legendre polynomials. The water mass variation can be expressed by equivalent water depth (rw =ra , where ra ¼ 1000 kg m3). The detailed expressions of the relevant variables can be found in Wahr et al. (1998).

20.4.3 GRACE dataset and applications

Since GRACE’s launch March 17, 2002, the   official GRACE Science Data System (SDS) DCnm cosðmlÞ þ DSnm sinðmlÞ Pnm ðcos qÞ continuously releases gravity solutions from (20.8) three different processing centers: the University

20.4 GRACE-based estimation

of Texas at Austin Center for Space Research, the GeoforschungsZentrum Potsdam, and the Jet Propulsion Laboratory. The provided time series consist of monthly and long-term mean sets of spherical harmonic coefficients of the global gravity potential of the Earth (Schmidt et al., 2008). These can be easily transferred into any gravity functional or surface mass anomalies in the space domain as required for the individual application. The GRACE data are divided into three levels. Level 1 data represent the raw data, collected from satellites, calibrated, and time-tagged in a nondestructive (or reversible) sense. It includes the intersatellite range, range rate, range acceleration, the nongravitational accelerations from each satellite, the pointing estimates, and the orbits. Level 2 data are the monthly gravity field estimates in form of spherical harmonic coefficients. All the Level 2 and

781

accompanying Level 1B products are released to the public. Since mission launch, several releases have been published by SDS. The most recent model generation provided by SDS is RL06 (published on April 26, 2018). Level 3 data are mass anomalies, and the products are available through several groups. Fig. 20.11 shows the data flow of the GRACE mission. Since its launch, the GRACE satellite data have been widely used to study water storage at various scales over different parts of the globe (Wahr et al., 2004; Han et al., 2005; Chambers, 2006; Schmidt et al., 2008; Ramillien et al., 2008). Frappart et al. (2008) determined spatiotemporal variations of water volume over the basin of the Negro River using combined observations from GRACE, T/P, and multisatellite inundation dataset. Alsdorf et al. (2010) combined GRACE satellite data with other remote

FIGURE 20.11 Data flow of Gravity Recovery and Climate Experiment (GRACE) mission. From http://www2.csr.utexas.edu/ grace/asdp.html, Used with permission from The University of Texas Center for Space Research

782

20. Water storage

Average Water Thickness (cm)

2002

4 2 0 –2 –4

10

2003

(A)

(B)

0 –10

10

(C)

0 –10

J

F M A M J

GRACE: Monthly

J A S O N D J

; Annual

F M A M J

J A S O N D

Hydrology Model: Monthly

; Annual

1.00 0.80 0.81 0.45 0.26 0.06 –0.10

FIGURE 20.12 Gravity Recovery and Climate Experiment (GRACE)ebased water variability (dots) within (A) the Mississippi River Basin, (B) the Amazon River basin, and (C) a drainage system flowing into the Bay of Bengal. The triangles represent the results from hydrological model. The best-fitting signals are in contrast for Gravity Recovery and Climate Experiment (GRACE)ederived results (blue) and model prediction (red).

sensing imagery to successfully explore seasonal water storage on the Amazon floodplain. Singh et al. (2012) investigated interannual water storage changes in the Aral Sea by using the GRACE satellite gravimetry in combination with satellite altimetry and optical imagery data. Panda and Wahr (2016) used 129 monthly gravity solutions from GRACE satellite to characterize spatiotemporal evolution of water storage changes in the Ganges River Basin of India. These studies have demonstrated the usefulness of the GRACE to study the water balance of large river basins, through combination with water stage data, precipitation data, and some other variables in terrestrial branch. It is demonstrated that the characteristics of GRACE restrict its meaningful application to study areas not smaller than 200,000 km2 (Singh et al., 2012), which is a big limitation for hydrological study of many lakes

and reservoirs with relatively smaller surface areas. In addition, the GRACE-derived results can also be used for validation of other results from indirect estimation or model simulation. Fig. 20.12 shows an example of water storage variations with respect to model-based results (Wahr et al., 2004). On the global scale, Schmidt et al. (2008) estimated the variations in global water storage and explored the spatiotemporal distribution of surface mass anomalies from GRACE dataset. Andersen and Hinderer (2005) estimated global gravity field changes using 15 monthly gravity field solutions from the GRACE twin satellites. The results demonstrated that GRACE is capable of capturing the changes in groundwater on interannual scales with an accuracy of 9 mm water thickness on spatial scales longer than 1300 km. More recently, Long et al. (2017)

References

FIGURE 20.13 The distribution of global water storage variation in May 2011.

analyzed the spatiotemporal variability in global total water storage using multiple GRACE products and global hydrological models. Fig. 20.13 shows the distribution of global water storage variation in May 2011 through Level-2 RL05 GRACE monthly field data (water abundance is represented by red, and water deficit is represented by blue).

20.5 Summary Satellite retrieval of water storage is an immature field but with rapid development. While remote sensing techniques have made great achievements in a variety of disciplines, accurate estimation of terrestrial water storage and its variations is facing several challenging tasks. First, terrestrial DEM is yet generally unavailable at the present. With international research efforts, the Shuttle Radar Topography Mission (SRTM) obtained DEMs on a near-global scale with a high spatial resolution of 30 m. However, there are void values over numerous land water surfaces. This is due that the DEM was undetectable with SRTM in these areas. Without reliable DEM, it is often impossible to obtain water storage over the areas. Second, the GRACE satellite often has difficulty in reliably estimating water storage over

783

land. Its coarse spatial resolution limits its applications to basins greater than 200,000 km2 at an altitude of 500 m (Rodell and Famiglietti, 2001). The detecting signal is not sensitive to small basins or areas. In addition, the monthly observation may not capture short flood event. Large rivers and lakes may have large variations of water storage in a few days during a specific flood event. Third, simultaneous estimation of surface area and water stage is under study. The altimeters currently in operation are solely limited to measures of elevation profile. Accurate retrieval of terrestrial water storage requires simultaneous estimation of surface area and water stage. This will be implemented with the SWOT WideSwath Altimeter, which was scheduled to be launched into space in 2021. In overall, we believe a promising prospect of satellite retrieval of global terrestrial water storage.

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