HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-21-4533-2017Recent changes in terrestrial water storage in the Upper Nile Basin: an
evaluation of commonly used gridded GRACE productsShamsudduhaMohammadm.shamsudduha@ucl.ac.ukhttps://orcid.org/0000-0002-9708-7223TaylorRichard G.https://orcid.org/0000-0002-9867-8033JonesDarrenLonguevergneLaurenthttps://orcid.org/0000-0003-3169-743XOworMichaelTindimugayaCallistInstitute for Risk and Disaster Reduction, University College London, London, UKDepartment of Geography, University College London, London, UKCentre for Geography, Environment and Society, University of Exeter, Exeter, UKCNRS – UMR 6118 Géosciences Rennes, Université de Rennes 1, Rennes, FranceDepartment of Geology & Petroleum Studies, Makerere University, Kampala, UgandaDirectorate of Water Resources Management, Ministry of Water & Environment, Entebbe, UgandaMohammad Shamsudduha (m.shamsudduha@ucl.ac.uk)12September20172194533454914March201721March201719June20178August2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/21/4533/2017/hess-21-4533-2017.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/21/4533/2017/hess-21-4533-2017.pdf
GRACE (Gravity Recovery and Climate Experiment) satellite data monitor
large-scale changes in total terrestrial water storage (ΔTWS),
providing an invaluable tool where in situ observations are limited.
Substantial uncertainty remains, however, in the amplitude of GRACE gravity
signals and the disaggregation of TWS into individual terrestrial water
stores (e.g. groundwater storage). Here, we test the phase and amplitude of
three GRACE ΔTWS signals from five commonly used gridded products
(i.e. NASA's GRCTellus: CSR, JPL, GFZ; JPL-Mascons; GRGS GRACE)
using in situ data and modelled soil moisture from the Global Land Data
Assimilation System (GLDAS) in two sub-basins (LVB: Lake Victoria Basin; LKB:
Lake Kyoga Basin) of the Upper Nile Basin. The analysis extends from January
2003 to December 2012, but focuses on a large and accurately observed
reduction in ΔTWS of 83 km3 from 2003 to 2006 in the Lake
Victoria Basin. We reveal substantial variability in current GRACE products
to quantify the reduction of ΔTWS in Lake Victoria that ranges from
80 km3 (JPL-Mascons) to 69 and 31 km3 for GRGS and
GRCTellus respectively. Representation of the phase in TWS in the
Upper Nile Basin by GRACE products varies but is generally robust with GRGS,
JPL-Mascons, and GRCTellus (ensemble mean of CSR, JPL, and GFZ
time-series data), explaining 90, 84, and 75 % of the variance
respectively in “in situ” or “bottom-up” ΔTWS in the LVB.
Resolution of changes in groundwater storage (ΔGWS) from GRACE
ΔTWS is greatly constrained by both uncertainty in changes in
soil-moisture storage (ΔSMS) modelled by GLDAS LSMs (CLM, NOAH, VIC)
and the low annual amplitudes in ΔGWS (e.g. 1.8–4.9 cm)
observed in deeply weathered crystalline rocks underlying the Upper Nile
Basin. Our study highlights the substantial uncertainty in the amplitude of
ΔTWS that can result from different data-processing strategies in
commonly used, gridded GRACE products; this uncertainty is disregarded in
analyses of ΔTWS and individual stores applying a single GRACE
product.
Introduction
Satellite measurements under the Gravity Recovery and Climate
Experiment (GRACE) mission have, since March 2002 (Tapley et al., 2004),
enabled remote monitoring of large-scale (i.e. GRACE footprint:
∼ 200 000 km2), spatio-temporal changes in total terrestrial
water storage (ΔTWS) at 10-day to monthly timescales (Longuevergne et
al., 2013; Humphrey et al., 2016). Over the last 15 years, studies in basins
around the world (Rodell and Famiglietti, 2001; Strassberg et al., 2007;
Leblanc et al., 2009; Chen et al., 2010; Longuevergne et al., 2010; Frappart
et al., 2011; Jacob et al., 2012; Shamsudduha et al., 2012; Arendt et
al., 2013; Kusche et al., 2016) have demonstrated that GRACE satellites trace
natural (e.g. drought, floods, glacier and ice melting, sea-level rise) and
anthropogenic (e.g. abstraction-driven groundwater depletion) influences on
ΔTWS. GRACE-derived TWS provides vertically integrated water storage
changes in all water-bearing layers (Wahr et al., 2004; Strassberg et
al., 2007; Ramillien et al., 2008) that include (Eq. 1) surface water storage
in rivers, lakes, and wetlands (ΔSWS), soil moisture storage
(ΔSMS), ice and snow water storage (ΔISS), and groundwater
storage (ΔGWS). Over the last decade, GRACE measurements have become
an important hydrological tool for quantifying basin-scale ΔTWS
(Güntner, 2008; Xie et al., 2012; Hu and Jiao, 2015) and are increasingly
being used to assess spatio-temporal changes in specific water stores
(Famiglietti et al., 2011; Shamsudduha et al., 2012; Jiang et al., 2014;
Castellazzi et al., 2016; Long et al., 2016; Nanteza et al., 2016) where
time-series records of other individual freshwater stores are available
(Eq. 1).
ΔTWSt=ΔGWSt+ΔISSt+ΔSWSt+ΔSMSt
GRACE-derived ΔTWS derive from monthly gravitational fields which can
be represented as spherical harmonic coefficients that are noisy as depicted
in north–south elongated linear features or “stripes” on monthly global
gravity maps (Swenson and Wahr, 2006; Wang et al., 2016). Post-processing of
GRACE SH data is therefore required. The most popular GRACE products are
NASA's GRCTellus land gravity solutions (i.e. spherical harmonics
based CSR, JPL, and GFZ), which require scaling factors to recover spatially
smoothed TWS signals (Swenson and Wahr, 2006; Landerer and Swenson, 2012).
Additionally, NASA's new monthly gridded GRACE product, Mass Concentration
blocks (i.e. Mascons), estimates terrestrial mass changes directly from
inter-satellite acceleration measurements and can be used without further
post-processing (Rowlands et al., 2010; Watkins et al., 2015). GRGS GRACE
products are also spherical harmonic-based, available at a 10-day time step,
and can also be used directly since gravity fields are stabilized during the
processing of GRACE satellite data (Lemoine et al., 2007; Bruinsma et
al., 2010).
Map of the study area encompassing the Lake Victoria Basin
(LVB) and Lake Kyoga Basin (LKB), and location of the in situ monitoring
stations. The Upper Nile Basin is marked by a rectangle (red) within the
entire Nile River Basin shown as a shaded relief index map. Piezometric
monitoring (red circles) and lake-level gauging (dark blue squares) stations
are shown on the map.
Restoration of the amplitude of GRCTellus TWS data, dampened by
spatial Gaussian filtering with a large smoothing radius (e.g.
300–500 km), is commonly achieved using scaling factors that derive
from a priori models of freshwater stores, usually a global-scale
land-surface model or LSM (Long et al., 2015). However, signal-restoration
methods are emerging that do not require hydrological models or LSMs
(Vishwakarma et al., 2016). Substantial uncertainty nevertheless persists in
the magnitude of applied scaling factors (e.g. GRCTellus) and
corrections (Long et al., 2015). Recent global-scale analyses have evaluated
variability in the amplitude of ΔTWS in various GRACE products
(Scanlon et al., 2016) and compared these with evidence from global
hydrological and land-surface models (Long et al., 2017); these studies
highlight well uncertainties in the amplitude of ΔTWS, but are not
reconciled to observations. In situ observations provide a valuable and
necessary constraint to the scaling of TWS signals over a particular study
area, as no consistent basis for ground-truthing these factors exists.
The disaggregation of GRACE-derived ΔTWS anomalies into individual
water stores (Eq. 1) is commonly constrained by the limited availability of
observations of terrestrial freshwater stores (i.e. ΔSWS, ΔSMS,
ΔGWS, and ΔISS). Indeed, a major source of uncertainty in the
attribution of GRACE ΔTWS derives from the continued reliance on
modelled ΔSMS derived from LSMs (i.e. CLM, NOAH, VIC, and MOSAIC)
under the Global Land Data Assimilation System or GLDAS (Rodell et al., 2004)
and remote-sensing products (Shamsudduha et al., 2012; Khandu et al., 2016).
Further, analyses of GRACE-derived ΔGWS often assume ΔSWS is
limited (Kim et al., 2009), yet studies in the humid tropics and engineered
systems challenge this assumption, showing that it can overestimate
ΔGWS (Shamsudduha et al., 2012; Longuevergne et al., 2013). Robust
estimates of ΔGWS from GRACE gravity signals have, to date, been
developed in locations where ΔSWS is well constrained by in situ
observations and groundwater is used intensively for irrigation so that
ΔGWS comprises a significant (> 10 %) proportion of ΔTWS
(Leblanc et al., 2009; Famiglietti et al., 2011; Shamsudduha et al., 2012;
Scanlon et al., 2015). In sub-Saharan Africa, intensive groundwater
withdrawals are restricted to a limited number of locations (e.g. irrigation
schemes, cities) and constrained by low-storage, low-transmissivity aquifers
in the deeply weathered crystalline rocks that underlie ∼ 40 % of
this region (MacDonald et al., 2012), including the Upper Nile Basin
(Fig. 1). Consequently, the ability of low-resolution GRACE gravity signals
to trace ΔGWS in these hard-rock environments is unclear. A recent
study (Nanteza et al., 2016) applies NASA's GRCTellus (CSR GRACE)
data over large basin areas (> 300 000 km2) of eastern Africa
and argues that ΔGWS can be estimated with sufficient reliability to
characterize regional groundwater systems after accounting for ΔSWS by
satellite altimetry and ΔSMS data from the GLDAS LSM ensemble (Rodell
et al., 2004).
Here, we exploit a large-scale reduction and recovery in surface water
storage that was recorded within Lake Victoria (Fig. 1), the world's second
largest lake by surface area (67 220 km2) (UNEP, 2013) and eighth
largest by volume (2760 km3) (Awange et al., 2008). This
well-constrained reduction in ΔSWS comprises a decline in lake level
of 1.2 m between May 2004 and February 2006, equivalent to a
lake-water volume (ΔSWS) loss of 81 km3 that resulted, in
part, from excessive dam releases (Fig. 2). We test the ability of current
GRACE products to represent the amplitude and phase of this voluminous and
well-constrained change in freshwater storage. Our analysis focuses on both
the Lake Victoria Basin (hereafter LVB) (256 100 km2) and Lake
Kyoga Basin (hereafter LKB) (79 270 km2) (Fig. 1). Applying in situ
observations of ΔSWS and ΔGWS combined with simulated
ΔSMS by the GLDAS LSMs, we assess (1) the ability of current gridded
GRACE products (i.e. GRCTellus, JPL-Mascons, and GRGS GRACE) to
measure a well-constrained ΔTWS in the Upper Nile Basin from 2003 to
2012, focusing on the unintended experiment within the LVB from 2003 to 2006;
and (2) the sensitivity of disaggregated GRACE ΔTWS signals to trace
ΔGWS in a deeply weathered crystalline rock aquifer system underlying
the Upper Nile Basin.
Observed daily total dam releases (blue line) and the agreed curve
(red line) at the outlet of Lake Victoria in Jinja from November 2007 to July
2009 (Owor et al., 2011).
The Upper Nile BasinHydroclimatology
The Upper Nile Basin, the headwater area of the
∼ 3 400 000 km2 Nile Basin (Awange et al., 2014), includes
both the LVB and LKB. Mean annual rainfall over the entire basin varies from
650 to 2900 mm (TRMM monthly rainfall; 2003–2012), with an average
of 1300 mm and a standard deviation of 354 mm (Fig. 3). Mean
annual gauged rainfall at different stations, Jinja, Bugondo, and Entebbe,
measured 1195, 1004, and 1541 mm respectively (Owor et al., 2011).
Rainfall over Lake Victoria is typically 25–30 % greater than that
measured in the surrounding catchment (Fig. 3), which is partially explained
by the nocturnal “lake breeze” effect (Yin and Nicholson, 1998; Nicholson
et al., 2000; Owor et al., 2011).
Estimates of mean annual evaporation from the surface of Lake Victoria vary
from 1260 mm (UNEP, 2013) to 1566 mm (Hoogeveen et
al., 2015), whereas mean annual evaporation from the surface of Lake Kyoga is
estimated to vary from 1205 mm (Brown and Sutcliffe, 2013) to
1660 mm (Hoogeveen et al., 2015). Evapotranspirative fluxes from the
surrounding swamps in Lake Kyoga are estimated to be much higher and
approximately 2230 mmyr-1 (Brown and Sutcliffe, 2013).
Mean annual rainfall for the period of 2003–2012 derived from TRMM
satellite observations. Greater annual rainfall is observed over much of Lake
Victoria and the north-eastern corner of the Lake Victoria Basin.
Annual rainfall is predominantly bimodal in distribution (Fig. 4), with two
distinct rainy seasons driven by the movement of the Intertropical
Convergence Zone (ITCZ) (Awange et al., 2013). Long rains (March–May) and
short rains (September–November) account for approximately 40 and 25 %
of annual rainfall respectively (Basalirwa, 1995; Indeje et al., 2000). The
latter rainfalls are particularly influenced by the El Niño–Southern
Oscillation (ENSO) and the Indian Ocean Dipole (IOD). GRACE-derived
ΔTWS within the LVB shows a statistical association (R2) of 0.56
with ENSO and 0.48 with IOD (Awange et al., 2014).
Lakes Victoria and Kyoga
Located between the 31∘39′ and 34∘53′ E longitudes, and
the 0∘20′ N and 3∘00′ S latitudes, Lake Victoria
(Fig. 1) is located in Tanzania, Uganda, and Kenya, where each accounts for
51, 43, and 6 % of the lake surface area respectively (Kizza et
al., 2012). Lake Victoria is relatively shallow, with a mean depth of
∼ 40 m and a maximum depth of 84 m (UNEP, 2013) akin to
many shallow, open surface-water bodies as well as permanent and seasonal
wetlands occupying low-relief plateaus across the Great Lakes Region of
Africa (Owor et al., 2011). Moreover, the western and north-western lake
bathymetry is characterized by even shallower depths of between 4 and
7 m (Owor, 2010). Hydrologically, lake input is dominated by direct
rainfall (84 % of total input); the remainder derives primarily from
river inflows as direct groundwater inflow (< 1 %) is negligible (Owor
et al., 2011). Approximately 25 major rivers flow into Lake Victoria, with a
total catchment area of ∼ 194 000 km2; the largest tributary,
the Kagera River, contributes ∼ 30 % of total river inflows (Sene
and Plinston, 1994). Lake Victoria outflow to Lake Kyoga occurs at Jinja
(Fig. 1).
Seasonal pattern (monthly mean from January 2003 to December 2012)
of TRMM-derived monthly rainfall, various GRACE-derived ΔTWS signals
(GRCTellus: ensemble mean of CSR, JPL, and GFZ; GRGS and JPL-Mascons (MSCN)
products), the bottom-up TWS, GLDAS LSM ensemble mean ΔSMS, in situ
ΔSWS, and a borehole-derived estimate of ΔGWS over the Lake
Victoria Basin.
Lake Kyoga (Fig. 1), located between the 32∘10′ and
34∘20′ E longitudes and the 1∘00′ and 2∘00′ N
latitudes, has a mean area of 1720 km2 with an estimated mean volume
of 12 km3 (Owor, 2010; UNEP, 2013). According to the recent global
HydroSHEDS (Hydrological data and maps based on shuttle elevation
derivatives at multiple scales) database, Lake Kyoga has a total surface area
of 2729 km2 (Lehner et al., 2008). Lake Kyoga comprises lake-zone
and through-flow conduit
areas. The lake zone in Lake Kyoga is very shallow, with a mean depth of
3.5–4.5 m (Owor, 2010). Lake Kyoga has a through-flow channel (mean
depth 7–9 m) where the main Victoria Nile River flows (Owor, 2010)
and acts as a linear reservoir with the annual water balance predominantly
governed by the discharge of the Victoria Nile from Lake Victoria. Whilst
numerous rivers flow into Lake Kyoga (e.g. rivers Mpologoma, Awoja, Omunyal,
Abalang, Olweny, Sezibwa, and Enget), the majority contributes a fraction of
their former volume upon reaching the lake (Krishnamurthy and Ibrahim, 1973)
due, in part, to evapotranspirative losses from fringe swamp areas
(4510 km2) surrounding the lake (UNEP, 2013).
Estimated areal extent (km2) of the Lake Victoria Basin
(LVB), the Lake Kyoga Basin (LKB), Lake Victoria, and Lake Kyoga.
The Upper Nile Basin is underlain primarily by deeply weathered crystalline
rock aquifer systems that have evolved through long-term, tectonically driven
cycles of deep weathering and erosion (Taylor and Howard, 2000). Groundwater
occurs within unconsolidated regoliths or “saprolite” and, below this, in
fractured bedrock, known as “saprock”. Bulk transmissivities of the
saprolite and saprock aquifers are generally low (1–20 m2d-1)
(Taylor and Howard, 2000; Owor, 2010) and field estimates of the specific
yield of the saprolite, the primary source of groundwater storage in these
aquifer systems, are 2 % based on pumping tests with tracers (Taylor et
al., 2010) and magnetic resonance sounding experiments (Vouillamoz et
al., 2014). Borehole yields are highly variable but generally low
(0.5–20 m3h-1), yet are of critical importance to the
provision of safe drinking water.
An observed reduction in TWS in the LVB
In 1954, the construction of the Nalubaale Dam (formerly Owen Falls Dam) at
the outlet of Lake Victoria at Jinja transformed the lake into a controlled
reservoir (Sene and Plinston, 1994). Operated as a run-of-river hydroelectric
project to mimic pre-dam outflows, the “agreed curve” between Uganda and
Egypt dictated dam releases that were controlled on a 10-day basis and
generally adhered to, with compensatory discharge releases to minimize any
departures, until the construction of the Kiira Dam at Jinja in 2002 (Sene
and Plinston, 1994; Owor et al., 2011).
The combined discharge of the Nalubaale and Kiira dams enabled total dam
releases (Fig. 2) to substantially exceed the agreed curve (Sutcliffe and
Petersen, 2007), and between May 2004 and February 2006 the lake level
dropped by 1.2 m (equivalent ΔSWS loss of 81 km3)
(Owor et al., 2011). Mean annual releases were 1387 m3s-1
(+162 % of the agreed curve) in 2004 and 1114 m3s-1
(+148 % of the agreed curve) in 2005. Sharp reductions in dam releases
in 2006 helped to arrest and reverse the lake-level decline, with lake levels
stabilizing by early 2007.
Data and methodsDatasets
We use publicly available time-series records of (1) GRACE TWS solutions from
a number of data-processing strategies and dissemination centres including
NASA's GRCTellus land solutions (RL05 for CSR, GFZ, version
DSTvSCS1409, RL05.1 for JPL; version DSTvSCS1411, and JPL-Mascons solution,
version RL05M_1.MSCNv01) as well as the French National Centre for Space
Studies (CNES) GRGS solution (version GRGS RL03-v1); (2) NASA's Global Land
Data Assimilation System (GLDAS) simulated soil moisture data from three
global land-surface models (LSMs) (CLM, NOAH, VIC); and (3) monthly
precipitation data from NASA's Tropical Rainfall Measuring Mission (TRMM)
satellite mission. We also employ in situ observations of lake levels and
groundwater levels from a network of river gauges and monitoring boreholes
operated by the Ministry of Water and Environment in Entebbe (Uganda).
Datasets are briefly described below.
Delineation of basin study areas
Delineation of the LVB and LKB was conducted in a geographic information
system (GIS) environment under an ArcGIS (v.10.3.1) environment using the
Hydrological Basins in Africa datasets derived from the HydroSHEDS
database (available at http://www.hydrosheds.org/) (Lehner et
al., 2006, 2008). Regional water bodies, including lakes Victoria and Kyoga
(Fig. 1), were spatially defined by the Inland Water dataset available
globally at country scale from DIVA-GIS (http://www.diva-gis.org/).
Computed areas of the basins and lake surface areas are summarized in Table 1
along with previously estimated figures from other studies.
GRACE-derived terrestrial water storage (TWS)
Twin GRACE satellites provide monthly gravity variations interpretable as
ΔTWS (Tapley et al., 2004) with an accuracy of ∼ 1.5 cm
(equivalent water thickness or depth) when spatially averaged (Wahr et
al., 2006). In this study, we apply five different monthly GRACE solutions
for the period of January 2003 to December 2012: post-processed, gridded
(1∘×1∘) GRACE-TWS time-series records from three
GRCTellus land solutions from CSR, JPL, and GFZ processing centres
(available at http://grace.jpl.nasa.gov/data) (Swenson and Wahr, 2006;
Landerer and Swenson, 2012), JPL-Mascons (Watkins et al., 2015; Wiese et
al., 2015), and GRGS GRACE products (CNES/GRGS release RL03-v1) (Biancale et
al., 2006).
GRCTellus land solutions are post-processed from two versions, RL05
and RL05.1 of spherical harmonics released by the University of Texas at
Austin Centre for Space Research (CSR), the German Research Centre for
Geosciences Potsdam (GFZ), and NASA's Jet Propulsion Laboratory (JPL)
respectively. GRCTellus gridded datasets are available at a monthly
time step at a spatial resolution of 1∘×1∘
(∼ 111 km at the Equator) though the actual spatial resolution
of the GRACE footprint is ∼ 450 km or
∼ 200 000 km2 (Scanlon et al., 2012). Post-processing of
GRCTellus GRACE datasets primarily involve (i) removal of
atmospheric pressure or mass changes based on the European Centre for
Medium-Range Weather Forecasts (ECMWF) model; (ii) a glacial isostatic
adjustment (GIA) correction based on a viscoelastic 3-D model of the Earth (A
et al., 2013); and (iii) an application of a destriping filter plus a 300 km
Gaussian to minimize the effect of correlated errors (i.e. destriping)
manifested by N–S elongated stripes on GRACE monthly maps. However, the use
of a large spatial filter and truncation of spherical harmonics leads to
energy removal, so scaling coefficients or factors are applied to the
GRCTellus GRACE-derived TWS data in order to restore attenuated
signals (Landerer and Swenson, 2012). Dimensionless scaling factors are
provided as 1∘×1∘ bins (see Fig. S1 in the
Supplement) that are derived from the Community Land Model (CLM4.0) (Landerer
and Swenson, 2012).
JPL-Mascons (version RL05M_1.MSCNv01) data processing also involves a
glacial isostatic adjustment (GIA) correction based on a viscoelastic 3-D
model of the Earth (A et al., 2013). JPL-Mascons applies no spatial filtering
as JPL-RL05M directly relates inter-satellite range-rate data to mass
concentration blocks or Mascons to estimate global monthly gravity fields in
terms of equal area 3∘×3∘ mass concentration
functions to minimize measurement errors. The use of Mascons and the special
processing result in better signal-to-noise ratios of the Mascon fields
compared to the conventional spherical harmonic solutions (Watkins et
al., 2015). For convenience, gridded Mascon fields are provided at a spatial
sampling of 0.5∘ in both latitude and longitude
(∼ 56 km at the Equator). As with GRCTellus GRACE
datasets, the neighbouring grid cells are not “independent” of each other
and cannot be interpreted individually at the 1∘ or 0.5∘ grid
scale (Watkins et al., 2015). Similar to GRCTellus GRACE (CSR, JPL,
GFZ) products, dimensionless scaling factors are provided as
0.5∘×0.5∘ bins (see Fig. S2) that are also derived
from the Community Land Model (CLM4.0) (Wiese et al., 2016). The gain factors
or scaling coefficients are multiplicative factors that minimize the
difference between the smoothed and unfiltered monthly ΔTWS variations
from “actual” land hydrology at a given geographical location (Wiese et
al., 2016).
GRGS/CNES GRACE monthly products (version RL03-v1) are processed and made
publicly available (http://grgs.obs-mip.fr/grace) by the French
Government space agency, National Centre for Space Studies or Centre National
d' Études Spatiales (CNES). The post-processing of GRGS data involves
taking into account of gravitational variations such as Earth tides, ocean
tides, and 3-D gravitational potential of the atmosphere and ocean masses
(Bruinsma et al., 2010). The remaining signals for time-varying gravity
fields therefore represent changes in terrestrial hydrology including snow
cover, baroclinic oceanic signals and effects of post-glacial rebound
(Biancale et al., 2006; Lemoine et al., 2007). Further details on the Earth's
mean gravity-field models can be found on the official website of GRGS/LAGEOS
(http://grgs.obs-mip.fr/grace/).
GRACE satellites were launched in 2002 to map the variations in Earth's
gravity field over its 5-year lifetime, but both satellites are still in
operation even after more than 14 years. However, active battery management
since 2011 has led the GRACE satellites to be switched off every 5–6 months
for 4–5-week durations in order to extend its total lifespan (Tapley et
al., 2015). As a result, GRACE ΔTWS time-series data have some missing
records that are linearly interpolated (Shamsudduha et al., 2012). In this
study, we derive ΔTWS time-series data as equivalent water depth (cm
of H2O) using the basin boundaries (GIS shapefiles) for masking the
1∘×1∘ grids.
Rainfall data
We apply the Tropical Rainfall Measuring Mission (TRMM) (Huffman et
al., 2007) monthly product (3B43 version 7) for the period of January 2003 to
December 2012 at 0.25∘×0.25∘ spatial resolution and
aggregate to 1∘×1∘ grids over the LVB and LKB. The
general climatology of the Upper Nile Basin is represented by a long-term
(2003–2012) mean annual rainfall (Fig. 3) and seasonal rainfall pattern
(Fig. 4). TRMM rainfall measurements show a good agreement with limited
observational precipitation records (Awange et al., 2008, 2014).
Monthly time-series datasets for the LVB from January 2003 to
December 2012: (a)GRCTellus GRACE-derived ΔTWS
(ensemble mean of CSR, GFZ, and JPL), GRGS and JPL-Mascons ΔTWS
time-series data; (b) GLDAS-derived ΔSMS (individual signals
as well as an ensemble mean of NOAH, CLM, and VIC);
(c) lake-level-derived ΔSWS; and
(d) borehole-derived ΔGWS time-series data. Note that monthly
rainfall records derived from TRMM satellite are plotted on
panel (d) where the dashed horizontal line represents the mean
monthly rainfall for the period of January 2003 to December 2012.
Monthly time-series datasets for the Lake Kyoga Basin (LKB) from
January 2003 to December 2012: (a)GRCTellus GRACE-derived
ΔTWS (ensemble mean of CSR, GFZ, and JPL), GRGS, and JPL-Mascons
ΔTWS time-series data; (b) GLDAS-derived ΔSMS
(individual signals as well as an ensemble mean of NOAH, CLM, and VIC);
(c) lake-level-derived ΔSWS; and
(d) borehole-derived ΔGWS time-series data. Note that monthly
rainfall records derived from the TRMM satellite are plotted in
panel (d) where the dashed horizontal line represents the mean
monthly rainfall for the period of January 2003 to December 2012.
Soil moisture storage (SMS)
NASA's Global Land Data Assimilation System (GLDAS) is an uncoupled
land-surface modelling system that drives multiple land surface models (GLDAS
LSMs: CLM, NOAH, VIC and MOSAIC) globally at high spatial and temporal
resolutions (3-hourly to monthly at 0.25∘×0.25∘ grid
resolution) and produces model results in near-real time (Rodell et
al., 2004). These LSMs provide a number of output variables which include
soil moisture storage (SMS). Similar to the approach applied in the analysis
of GRACE-derived ΔTWS analysis in the Bengal Basin (Shamsudduha et
al., 2012), we apply simulated monthly ΔSMS records at a spatial
resolution of 1∘×1∘ from three GLDAS LSMs: the
Community Land Model (CLM, version 2) (Dai et al., 2003), NOAH
(version 2.7.1) (Ek et al., 2003) and the Variable Infiltration Capacity
(VIC) model (version 2.7.1) (Liang et al., 2003). The respective depths of
modelled soil profiles are 3.4, 2.0, and 1.9 m in CLM (10 vertical
layers), NOAH (4 vertical layers), and VIC (version 1.0) (3 vertical layers).
Because of the absence of in situ soil moisture data in the study areas, we
apply an ensemble mean of the aforementioned three LSMs-derived simulated
ΔSMS time-series records (see Figs. 5 and 6) in order to disaggregate
GRACE ΔTWS signals in the LVB and LKB.
Surface water storage (SWS)
Daily time series of ΔSWS are computed from in situ (gauged)
lake-level observations at Jinja for Lake Victoria and Bugondo for Lake Kyoga
(Figs. 1 and 2) compiled by the Ugandan Ministry of Water and Environment
(Directorate of Water Resources Management). Mean monthly anomalies for the
period of January 2003–December 2012 were computed as an equivalent water
depth using Eq. (2). Missing data in the time series (2003–2012) records are
linearly interpolated. For instance, in the case of monthly ΔSWS
derived from Lake Kyoga water levels, there is one missing record (December
2005).
ΔSWS=ΔLakelevel⋅LakeareaTotalbasinarea
Details of groundwater and lake-level monitoring stations located in
the Lake Victoria Basin and Lake Kyoga Basin.
Time series of ΔGWS are constructed from in situ piezometric records
from 6 monitoring wells located in the LVB and LKB where near-continuous,
daily observations exist from January 2003 to December 2012 and have been
compiled by the Ugandan Ministry of Water and Environment (Directorate of
Water Resources Management) (Owor et al., 2009, 2011). Monitoring boreholes
were installed into weathered, crystalline rock aquifers that underlie much
of the LVB and LKB, and are remote from local abstraction. As such, they
represent variations in groundwater storage influenced primarily by climate
variability. Mean monthly anomalies of ΔGWS, standardized to mean
records from January 2003 to December 2012, were derived from
near-continuous, daily observations at Entebbe, Rakai, and Nkokonjeru for the
LVB and at Apac, Pallisa, and Soroti for the LKB (Figs. 1 and S3; Table 2).
In the Lake Kyoga Basin, piezometric records from three sites show
consistency in the seasonality and amplitude of groundwater storage changes
plotted as monthly groundwater-level anomalies relative to the mean for the
period from January 2003 to December 2012. In the Lake Victoria Basin,
groundwater-level records from two sites (Entebbe, Nkokonjeru) are similar in
their phase and amplitude, and are influenced by changes in the level of Lake
Victoria as demonstrated by Owor et al. (2011). The groundwater-level record
from Rakai represents local semi-arid conditions that exist within catchment
areas (e.g. the Ruizi River) draining to the western shore of Lake Victoria
in Uganda. Although there are differences in the phase of groundwater-level
fluctuations between the semi-arid site at Rakai and both Entebbe and
Nkokonjeru (as well as the three sites in the Lake Kyoga Basin), annual
amplitudes are similar.
The groundwater-level time series data are a sub-set of the total number of
available monitoring-well records in the LVB and LKB and selected on the
basis of (i) the completeness and quality of the records from 2003 to 2012,
and (ii) rigorous review of groundwater-level records conducted at a
dedicated workshop at the Ministry of Water & Environment in January 2013.
These records represent shallow groundwater-level observations within the
saprolite that is dynamically connected to surface waters (Owor et
al., 2011). Long time-series records of groundwater levels over the period
from 2003 to 2012 from western Kenya, northern Tanzania, Rwanda, and Burundi
have not been identified despite intensive investigations carried out by
The Chronicles Consortium.
The Chronicles Consortium:
https://www.un-igrac.org/special-project/chronicles-consortium
The
partial spatial coverage in quality-controlled piezometry, especially for the
LVB, represents an important limitation in our analysis.
Mean monthly anomalies were translated into an equivalent water depth (Eq. 3)
by applying a range of specific yield (Sy) values (1–6 % with an
average of 3 %), although estimates of Sy in hard-rock environments
are observed to vary from < 2 to 8 % (Taylor et al., 2010, 2013;
Vouillamoz et al., 2014) using Eq. (3). Missing data in the time series were
linearly interpolated. In the case of monthly ΔGWS that were derived
from borehole (n=6) observations, missing records range from 1 to
9 months (120 months in 2003–2012), with three boreholes (Soroti, Rakai, and
Nkonkonjero) with time-series records ending in June–July 2010.
ΔGWS=Δh⋅Sy⋅LandareaTotalbasinarea
MethodologiesGRACE ΔTWS estimation
First, the 1∘×1∘ gridded monthly anomalies of
GRACE-derived ΔTWS and GLDAS LSM-derived ΔSMS are masked over
the area of the LVB and LKB. GRACE ΔTWS along with GLDAS ΔSMS
are extracted for the marked 1∘×1∘ grid cells for the
LVB and LKB and the grid values are spatially aggregated to form time series
of monthly anomalies ΔTWS and ΔSMS.
GRCTellus GRACE ΔTWS gridded data are scaled using
dimensionless, gridded scaling factors. Several GRACE studies (Rodell et
al., 2009; Sun et al., 2010; Shamsudduha et al., 2012) have applied scaling
factors in three different ways: (1) a single scaling factor based on
regionally averaged time series, (2) spatially distributed or gridded scaling
factors based on time series at each grid point, and (3) gridded-gain factors
estimated as a function of time or of temporal frequency (Landerer and
Swenson, 2012; Long et al., 2015). In this study, we apply a spatially
distributed scaling approach (method 2 above) to generate basin-averaged
ΔTWS time-series records for GRCTellus (CSR, JPL, GFZ)
products. Scaling factors provided at 1∘×1∘ grids are
applied to each corresponding GRACE ΔTWS grid for NASA's
GRCTellus products in order to restore attenuated signals during the
post-processing (Landerer and Swenson, 2012) using Eq. (4). Similarly,
provided scaling factors are applied to JPL-Mascons ΔTWS time-series
data but at 0.5∘×0.5∘ grid resolution. No scaling
factors were applied to GRGS GRACE ΔTWS as the monthly gravity
solutions have already been stabilized during their generation
process.
g1x,y,t=gx,y,t⋅sx,y
Here, g1(x,y,t) represents each un-scaled grid where x represents
longitude, y represents latitude, t represents time (month), and s(x,y) is the corresponding scaling factor.
For the three GRCTellus gridded products (i.e. CSR, GFZ,
and JPL solutions), we apply an ensemble mean of scaled GRACE ΔTWS as
our exploratory analyses reveal that ΔTWS time-series records over the
Lake Victoria Basin are highly correlated (r>0.95, p value < 0.001)
with each other. Additionally, a small (ranges from 1.3 to 1.9 cm)
root mean square error (RMSE) among the GRACE ΔTWS datasets suggests
substantial similarities in phase and amplitude.
Estimation of ΔGWS from GRACE
Estimation of groundwater storage changes (ΔGWS) from GRACE
measurements is conducted using Eq. (5) in which ΔTWSt is derived
from gridded GRACE products (spatially scaled ΔTWS for
GRCTellus and JPL-Mascons but unscaled ΔTWS for GRGS),
ΔSMSt is an ensemble mean of three GLDAS LSMs (CLM, NOAH, VIC),
and ΔSWSt is area-weighted, in situ surface water storage
estimated from lake-level records using Eq. (2).
ΔGWSt=ΔTWSt-ΔSWSt+ΔSMSt
Reconciliation of GRACE ΔTWS disaggregation
Reconciling GRACE-derived TWS with ground-based observations is limited by
the paucity of in situ observations of SMS, SWS, and GWS in many
environments. In addition, direct comparisons between in situ observations of
ΔSMS, ΔSWS, and ΔGWS and gridded GRACE ΔTWS
anomalies are complicated by substantial differences in spatial scales, which
need to be considered prior to analysis (Becker et al., 2010). For example,
individual groundwater-level monitoring boreholes may represent, depending on
borehole depth, a sensing area of several tens of square kilometres (Burgess
et al., 2017), whereas the typical GRACE footprint is
∼ 200 000 km2. The disaggregation of GRACE ΔTWS into
individual water stores can also propagate errors to disaggregated
components. Here, we construct “in situ” or “bottom-up” ΔTWS (i.e.
combined signals of ΔSMS, ΔSWS, and ΔGWS) for the Lake
Victoria Basin and attempt to reconcile with GRACE-derived ΔTWS. One
feature of GRACE ΔTWS among the three solutions we apply in this study
is the considerable variation in annual amplitudes that exist over the period
of 2003–2012.
In addition, for the GRCTellus products, we conduct unconventional
scaling experiments, outlined below in an attempt to reconcile satellite and
in situ measures and to shed light on the uncertainty in ΔTWS
amplitudes of the GRCTellus GRACE products. The ΔTWS signals
in CSR, JPL, and GFZ products are greatly attenuated due to spatial smoothing
and the amplitude is substantially smaller compared to JPL-Mascons and GRGS
products. In the first scaling experiment, we apply an additional,
basin-averaged, multiplicative scaling factor to ΔTWS ranging from 1.1
to 2.0 and employ RMSE to assess their relative performance. With reference
to the GRCTellus GRACE ΔTWS and bottom-up ΔTWS
relationship, the scaling factor producing the lowest RMSE between the two
time series is employed. Secondly, it is observed that, in the LVB, ΔSWS is the largest contributor, representing ∼ 50 % variance in
the in situ or bottom-up ΔTWS time-series signal. GRACE ΔTWS
analyses commonly apply the same scaling factor as ΔTWS to all other
individual components (Landerer and Swenson, 2012). Therefore, under the
scaling experiment, we apply to in situ ΔSWS spatially averaged
scaling factors representative of (i) Lake Victoria and its surrounding grid
cells (experiment 1: s=0.71; range 0.02–1.5), and (ii) the open-water
surface of Lake Victoria without surrounding grid cells (experiment 2: s=0.11; range 0.02–0.30). Furthermore, we find that the amplitude of monthly
anomalies of ΔSWS +ΔSMS combined substantially exceed
ΔTWS (see Fig. S4), particularly for the GRCTellus GRACE
ΔTWS signal that is greatly smoothed due to filtering. This
discrepancy is pronounced over the period of 2003–2006, and when applied to
estimate GRACE-derived ΔGWS, produces steep, rising trends in the
estimated ΔGWS (i.e. GRACE ΔTWS-(ΔSWS+ΔSMS)), whereas borehole observations of groundwater levels show
a declining trend and are of much a lower amplitude over the same period.
Results
Monthly time-series records (January 2003–December 2012) are presented in
Figs. 5 and 6 respectively for the LVB and LKB of (a) GRACE ΔTWS from
GRCTellus GRACE ΔTWS (ensemble mean of CSR, GFZ, and JPL
solutions), GRGS and JPL-Mascons, (b) GLDAS land-surface models (LSMs)
derived ΔSMS (ensemble mean of three LSMs: NOAH, CLM, VIC), (c) in
situ ΔSWS from lake levels records, and (d) in situ ΔGWS
borehole observations. Monthly rainfall derived from TRMM satellite
observations over the same period are shown on the bottom panel (d).
Time-series records of all ΔTWS components and rainfall are aggregated
for the LVB to represent the average seasonal (monthly) pattern of each
signal (Fig. 4) that shows an obvious lag (∼ 1 month) between peak
rainfall (March–April) and ΔTWS and its individual components.
Mean annual (2003–2012) amplitudes of various GRACE-derived ΔTWS
signals, bottom-up ΔTWS, ensemble mean of simulated ΔSMS, in
situ ΔSWS, and ΔGWS time-series records (Figs. 5 and 6) are
presented (see Table S1 in the Supplement) for both the LVB and LKB. The mean
annual amplitude of GRACE ΔTWS ranges from 11 to 21 cm among
GRCTellus, GRGS, and JPL-Mascons GRACE products in the LVB, and from
8.4 to 16.4 respectively in the LKB. The mean annual amplitude of in situ
ΔSWS is much greater (14.8 cm) in the LVB than in the LKB
(3.8 cm). The GLDAS LSM-derived ensemble mean ΔSMS amplitude
in the LVB is 7.9 and 7.3 cm in the LKB. The standard deviation in
ΔSMS varies substantially in the LVB (1.2, 4.2, and 2.9 cm)
and LKB (1.3, 4.7, and 4.0 cm) for the CLM, NOAH, and VIC models
respectively. The mean annual amplitude of in situ ΔGWS ranges from
4.4 cm (LVB) to 3.5 cm (LKB).
Time-series correlation (Pearson) analysis over various periods of interests
(decadal: 2003–2012; well-constrained SWS reduction or the period of the
unintended experiment: 2003–2006; controlled dam operation: 2007–2012)
reveals that GRACE-derived ΔTWS signals are strongly correlated in
both the LVB and LKB (see Figs. S5–S10). For example, in the LVB, in situ
ΔSWS shows a statistically significant (p value < 0.001) strong
correlation (r= 0.77–0.92) with all GRACE-ΔTWS time-series
(2003–2012) records. Similarly, simulated ΔSMS shows statistically
significant (p value < 0.001) strong correlation (r= 0.70–0.78)
with ΔTWS time-series records. In contrast, in situ ΔGWS shows
statistically significant (p value < 0.001) but moderate correlation
(r=0.63–0.69) with ΔTWS time-series records. Correlation among the
variables shows similar statistically significant (p value < 0.001) but
wide-ranging associations for the periods of the unintended experiment
(2003–2006) and controlled dam operation (2007–2012). In the LKB, however,
correlation among in situ ΔSWS and GRACE ΔTWS time-series
records is statistically significant (p value < 0.05) but poor in
correlation strength (r= 0.22–0.34). In situ ΔGWS shows
statistically significant (p value < 0.001) strong correlation
(r= 0.64–0.69) with GRACE ΔTWS time-series records.
Time-series records of all three ΔTWS from five GRACE products and
bottom-up ΔTWS time-series records in both the LVB and LKB are shown
in Fig. 7; results of temporal trends are summarized in Table 3.
Statistically significant (p value < 0.05) declining trends (-4.1 to
-11.0 cmyr-1 in the LVB; -2.1 to -4.6 cmyr-1
in the LKB) are consistently observed during the period of 2003–2006. Trends
are all positive in GRACE ΔTWS and bottom-up ΔTWS time-series
records over the recent period of controlled dam operation (2007–2012) in
both the LVB and LKB. The overall, decadal (2003–2012) trends are slightly
rising (0.04–1.00 cmyr-1) in the LVB but nearly stable
(-0.01 cmyr-1) in GRCTellusΔTWS and slightly
declining (-0.56 cmyr-1) in bottom-up ΔTWS over the
LKB. In addition, short-term volumetric trends (2003–2006) in GRACE and
bottom-up ΔTWS as well as simulated ΔSMS and in situ
ΔSWS are declining whereas in situ ΔGWS and rainfall anomalies
show slightly rising trends over the same period in the LVB (see Figs.
S11–S12). Similar trends are reported in various signals over the LKB, but
magnitudes are much smaller compared to that of the LVB, which is 3 times
larger in size than the LKB. Volumetric declines in ΔTWS in the LVB
for the period 2003–2006 are: 83 km3 (bottom-up), 80 km3
(JPL-Mascons), 69 km3 (GRGS) and 31 km3 (GRCTellus
ensemble mean of CSR, JPL and GFZ products).
Linear trends (cmyr-1) in GRACE ΔTWS and
bottom-up ΔTWS in the Lake Victoria Basin and Lake Kyoga Basin over
various time periods (statistically significant trends; p values < 0.05
are marked by an asterisk).
Comparison among time-series records of ΔTWS from
GRCTellus (ensemble mean of CSR, GFZ, and JPL), GRGS and JPL-Mascons
GRACE products and bottom-up ΔTWS for the LVB (a), and the
LKB (b) for the period of January 2003 to December 2012. The
vertical grey lines represent monthly rainfall anomalies in the LVB and LKB.
Estimates of in situ ΔGWS and GRACE-derived ΔGWS
time-series records (January 2003 to December 2012) in the LVB show
substantial variations among themselves. An ensemble mean ΔSMS (three
GLDAS LSMs: CLM, NOAH, and VIC) and an unscaled ΔSWS are applied in
the disaggregation of ΔGWS using the GRCTellus GRACE
(ensemble mean of CSR, GFZ, and JPL) and JPL-Mascons products.
Taylor diagram shows strength of statistical association,
variability in amplitudes of time-series records and agreement among the
reference data, bottom-up ΔTWS and GRCTellus GRACE-derived
ΔTWS (ensemble mean of CSR, GFZ, and JPL, GRGS and JPL-Mascons
ΔTWS time-series records), simulated ΔSMS (ensemble mean of
NOAH, CLM and VIC), in situ ΔSWS, and in situ ΔGWS over the
LVB. The solid arcs around the reference point (black square) indicate
centred root mean square (RMS) differences among bottom-up ΔTWS and
other variables, and the dashed arcs from the origin of the diagram indicate
variability in time-series records. Data for the LVB are only shown in this
diagram.
Linear regression reveals that the association between GRACE-derived
ΔTWS and bottom-up ΔTWS is stronger in the LVB (R2= 0.75–0.90) than in the LKB (R2= 0.56–0.62) (see Table S1). GRACE
ΔTWS is unable to explain natural variability in bottom-up ΔTWS
in the LKB, though this may be explained by the fact that SWS in Lake Kyoga
is influenced by dam releases from the LVB. Multiple linear regression and
the analysis of variance (ANOVA) reveal that the relative proportion of
variability in the bottom-up ΔTWS time-series record can be explained
by ΔSWS (92.6 %), ΔSMS (6.5 %), and ΔGWS
(0.66 %) in the LVB; and by 47.9, 48.5, and 3.6 % respectively in the
LKB. These results are indicative only as these percentages can be biased by
the presence of strong correlation among variables and the order of these
variables listed as predictors in the multiple linear regression models.
Disaggregation of ΔGWS from GRACE ΔTWS time-series record from
each product has been carefully considered and estimated following Eq. (5).
No further additional scaling factors, as described in the “scaling
experiment” section (see results of scaling experiment in Fig. S13) are
applied in the final disaggregation of ΔGWS from GRACE ΔTWS
signals. Results of Pearson correlation analysis of the time-series record
(2003–2012) of in situ ΔGWS in the LVB show statistically
insignificant and poor correlation (r=0.11, p value = 0.25) to
JPL-Mascons and an inverse correlation with both the ensemble
GRCTellus (r=-0.55, p value < 0.001) and GRGS
(r=-0.27, p value = 0.003) GRACE-derived estimates of ΔGWS
(Fig. 8). In contrast, in the LKB, in situ ΔGWS time-series record
shows statistically significant but weak correlations to JPL-Mascons (r=0.34, p value < 0.001) and GRGS (r=0.39, p value < 0.001)
GRACE-derived ΔGWS but shows an inverse correlation (r=-0.21,
p value = 0.02) to GRCTellusΔGWS (see Fig. S14).
Furthermore, RMSE among various GRACE-derived estimates of ΔGWS and in
situ ΔGWS ranges from 7.2 cm (GRACE ensemble), 3.8 cm
(GRGS) to 8.2 cm (JPL-Mascons) in the LVB, and from 3.2 cm
(GRACE ensemble), 5.3 cm (GRGS) to 5.4 cm (JPL-Mascons) in
the LKB.
Discussion
We apply five different gridded GRACE products (GRCTellus – CSR,
JPL, and GFZ; GRGS and JPL-Mascons) to test ΔTWS signals for the Lake
Victoria Basin (LVB) comprising a large and accurately observed reduction
(83 km3) in ΔTWS from 2003 to 2006. Our analysis reveals that
all GRACE products capture this substantial reduction in terrestrial water
mass, but the magnitude of GRACE ΔTWS among GRACE products varies
substantially. For example, GRCTellus underrepresents greatly
(63 %) the reduction of 83 km3 in bottom-up ΔTWS, whereas
GRGS and JPL-Mascons GRACE products underrepresent this by 17 and 4 %
respectively. Previous studies in the Upper Nile Basin have relied upon a
single GRACE product such as GRCTellus CSR (Nanteza et al., 2016)
and GFZ (version (RL04) (Awange et al., 2014) without considering uncertainty
in the seasonal amplitude of TWS associated with the processing of different
GRACE products. Over a longer period (2003–2012) in the Upper Nile Basin,
all GRACE products correlate well with bottom-up ΔTWS but, similar to
the unintended experiment, variability in amplitude is considerable (Fig. 9).
The average (2003–2012) annual amplitude of ΔTWS is substantially
dampened (i.e. 45 % less than bottom-up ΔTWS) in
GRCTellus GRACE products relative to GRGS (4 %) and JPL-Mascons
(27 % more than bottom-up ΔTWS) products in the LVB.
The “true” amplitude in the GRCTellusΔTWS signal is
generally reduced during the post-processing of GRACE spherical harmonic
fields, primarily due to spatial smoothing by a large-scale (e.g.
300 km) Gaussian filter and truncation of gravity fields at a higher
(degree 60 = 300 km) spectral degree (Swenson and Wahr, 2006;
Landerer and Swenson, 2012). Despite the application of scaling factors based
on CLM v.4.0 to amplify GRCTellusΔTWS amplitudes at
individual grids, the basin-averaged (LVB) time-series record represents only
75 % variability in bottom-up ΔTWS. Scaling experiments conducted
here reveal that GRCTellusΔTWS requires an additional
multiplicative factor of 1.7 in order to match bottom-up ΔTWS with a
minimum RMSE (5.8 cm). On the other hand, NASA's new gridded GRACE
product, JPL-Mascons, which applies an a priori constraint in space and time
to derive monthly gravity fields and undergoes some degree of spatial
smoothing (Watkins et al., 2015), represents nearly 83 % variability in
bottom-up ΔTWS. In contrast, the GRGS GRACE product, which applies
truncation at degree 80 (∼ 250 km), does not suffer from any
large-scale spatial smoothing, and is able to represent well (90 %) the
variability in bottom-up ΔTWS in the LVB.
A priori corrections of GRCTellus ensemble mean GRACE signals using
a set of LSM-derived scaling factors (i.e. amplitude gain) can lead to
substantial uncertainty in ΔTWS (Long et al., 2015). We show that the
amplitude of simulated terrestrial water mass over the Upper Nile Basin
varies substantially among various LSMs (see Fig. S15). Most of these LSMs
(GLDAS models: CLM, NOAH, VIC) do not include surface water or groundwater
storage (Scanlon et al., 2012). Although CLM (v.4.0 and 4.5) includes a
simple representation (i.e. shallow unconfined aquifer) of groundwater (Niu
et al., 2007; Oleson et al., 2008), it does not consider recharge from
irrigation return flows. In addition, many of these LSMs do not consider
lakes and reservoirs and, most critically, LSMs are not reconciled with in
situ observations.
The combined measurement and leakage errors, √(bias2+leak2) (Swenson and Wahr, 2006) for GRCTellusΔTWS
based on CLM4.0 model for the LVB and LKB are 7.2 and 6.6 cm
respectively. These values, however, do not represent mass leakage from the
lake to the surrounding area within the basin itself. A sensitivity analysis
of GRCTellus and GRGS signals reveal that signal leakage occurs from
lake to its surrounding basin area as well as between basins. For instance,
GRACE signal leakage into the LKB from the LVB, which is 3 times larger in
area than the LKB, is 3.4 times bigger for both GRCTellus GRACE and GRGS
products. Furthermore, the analysis shows that leakage from Lake Victoria to
the LVB for GRCTellus is substantially greater than GRGS product by
a factor of ∼ 2.6. In other words, 1 mm change in the level of Lake
Victoria represents an equivalent change of 0.12 mm in ΔTWS in
the LVB for GRCTellus compared to 0.32 mm for GRGS.
Consequently, changes in the amplitude of GRGS ΔTWS are much greater
(∼ 38 %) than GRCTellus. During the observed reduction in
ΔTWS (83 km3) from 2003 to 2006, the computed volumetric
reduction for GRGS is found to be 69 km3 whereas it is
31 km3 for GRCTellus.
Another source of uncertainty that contributes toward ΔTWS anomalies
in GRACE analysis is the choice of simulated ΔSMS from various
global-scale LSMs (e.g. Shamsudduha et al., 2012; Scanlon et al., 2015). For
example, the mean annual (2003–2012) amplitudes in simulated ΔSMS in
GLDAS LSMs (CLM, NOAH, VIC) vary substantially in the LVB (3.5, 10.2, and
10.5 cm) and LKB (3.7, 10.6, and 7.7 cm) respectively. Due to
an absence of a dedicated monitoring network for soil moisture in the Upper
Nile Basin, this study, like many other GRACE studies, is resigned to
applying simulated ΔSMS from multiple LSMs, arguing that the use of an
ensemble mean minimizes the error associated with ΔSMS (Rodell et
al., 2009).
Computed contributions of ΔGWS to ΔTWS in the Upper Nile Basin
are low (< 10 %). GRACE-derived estimates of ΔGWS from all
three products (GRCTellus, GRGS, and JPL-Mascons)
correlate very weakly with in situ ΔGWS in both the LVB and LKB. One
curious observation in the LVB during the unintended experiment (2003–2006)
is that in situ ΔGWS rises, whereas in situ ΔSWS and simulated
ΔSMS decline. The available evidence in groundwater-level records
(e.g. Entebbe, Uganda) suggests that rainfall-generated groundwater recharge
led to an increase in ΔGWS, while dam releases exceeding the agreed
curve continued to reduce ΔSWS (Owor et al., 2011).
Uncertainties in the estimation of GRACE-derived ΔGWS remain in
(i) accurate representation of the largest individual signal of in situ
ΔSWS in the disaggregation of GRACE ΔTWS signals as it can
limit the propagation of uncertainty in simulated ΔSMS, (ii) simulated
ΔSMS by GLDAS land-surface models, (iii) the very limited spatial
coverage in piezometry to represent in situ ΔGWS, and (iv) applied
Sy (3 % with a range from 1 to 6 %) to convert in situ
groundwater levels to ΔGWS. The lack of any strong correlation in
GRACE-derived ΔGWS and in situ ΔGWS time-series records
indicates that the magnitude of uncertainty is larger than the overall
variability in ΔGWS in low-storage, low-transmissivity weathered
crystalline aquifers within the Upper Nile Basin. Furthermore, statistically
significant but negative correlations in both the LVB and LKB arise from a
positive change in GRACE-derived ΔGWS when in situ ΔGWS is
declining (e.g. 2003–2006 in the LVB; 2008–2010 in the LKB). This
inconsistency suggests that the “true” GRACE ΔTWS signal is weakened
during processing and that the combined ΔSWS +ΔSMS signal
is greater than ΔTWS, mathematically resulting in a positive estimate
of ΔGWS. In contrast to the assertions of Nanteza et al. (2016),
applying the GRCTellus CSR solution, we find that this uncertainty
prevents robust resolution of ΔGWS from GRACE ΔTWS in these
complex hydrogeological environments of eastern Africa. Despite substantial
efforts to improve groundwater-level monitoring and to collate existing
groundwater-level records across Africa, we recognize that understanding of
in situ ΔGWS remains greatly constrained by limitations in current
observational networks and records. Since present uncertainties and
limitations identified in the Upper Nile Basin occur in many of the weathered
hard-rock aquifer environments that underlie 40 % of sub-Saharan Africa
(MacDonald et al., 2012), tracing of ΔGWS using GRACE in these areas
is unlikely to be robust until these uncertainties and limitations are better
constrained.
Conclusions
The analysis of a large, accurately recorded reduction of
1.2 m in the water level of Lake Victoria, equivalent to a
ΔSWS decline of 81 km3 from 2004 to 2006, exposes substantial
variability among five commonly used gridded GRACE products
(GRCTellus CSR, JPL, GFZ; GRGS; JPL-Mascons) to quantify the
amplitude of changes in terrestrial water storage (ΔTWS). Around this
event, we estimate an overall decline in “in situ” or “bottom-up”
ΔTWS (i.e. in situ ΔSWS and ΔGWS; simulated ΔSMS)
over the LVB of 83 km3 from 2003 to 2006. This value compares
favourably with JPL-Mascons GRACE ΔTWS (80 km3), is
underrepresented by GRGS GRACE ΔTWS (69 km3), and is
substantially underrepresented by the ensemble mean of GRCTellus
GRACE ΔTWS (31 km3). Attempts to better reconcile
GRCTellus GRACE ΔTWS to bottom-up ΔTWS through scaling
techniques are unable to represent adequately the observed amplitude in
ΔTWS but highlight the uncertainty in the amplitude of gridded GRACE
ΔTWS datasets generated by various processing strategies.
From 2003 to 2012, GRGS, JPL-Mascons, and GRCTellus GRACE products
trace well the phase in bottom-up ΔTWS in the Upper Nile Basin that
comprises both the LVB and the LKB. In the LVB, for example, each explains
90 % (GRGS), 83 % (JPL-Mascons), and 75 % (GRCTellus
ensemble mean of CSR, JPL, and GFZ) of the variance respectively in bottom-up
ΔTWS. The relative proportion of variability in bottom-up ΔTWS
(variance 120 cm2 LVB, 24 cm2 LKB) is explained by in situ
ΔSWS (93 % LVB; 49 % LKB), GLDAS ensemble mean ΔSMS
(6 % LVB; 48 % LKB), and in situ ΔGWS (∼ 1 % LVB;
4 % LKB); these percentages are indicative and can vary as individual TWS
components are strongly correlated and the order of explanatory variables in
the regression equation can affect the analysis of variance (ANOVA). In situ
ΔGWS contributes minimally to ΔTWS and is only moderately
associated with GRACE ΔTWS (strongest correlation of r=0.39,
p value < 0.001). The resolution of ΔGWS from GRACE ΔTWS
in the Upper Nile Basin relies upon robust measures of ΔSWS and
ΔSMS; the former is observed in situ, whereas the latter is limited by
uncertainty in simulated ΔSMS, represented here and in many GRACE
studies by an ensemble mean of GLDAS LSMs. Mean annual amplitudes in observed
ΔGWS (2003–2012) from limited piezometry for the low-storage and
low-transmissivity aquifers in deeply weathered crystalline rocks that
underlie the Upper Nile Basin are small (1.8–4.9 cm for Sy=0.03) and, given the current uncertainty in simulated ΔSMS, are
beyond the limit of what can be reliably quantified using current GRACE
satellite products.
Our examination of a large, mass-storage change (2003–2006) observed in the
Lake Victoria Basin highlights substantial variability in the measurement of
ΔTWS using different gridded GRACE products. Although the phase in
ΔTWS is generally well recorded by all tested GRACE products,
substantial differences exist in the amplitude of ΔTWS that influence
the disaggregation of individual terrestrial stores (e.g. groundwater
storage) and the estimation of temporal trends in TWS. Analyses that solely
rely upon a single solution disregard the uncertainty in ΔTWS
associated with GRACE signal processing. We note, for example, that the
stronger filtering of the large-scale (∼ 300 km) gravity signal
associated with GRCTellus results in greater signal leakage relative
to GRGS and JPL-Mascons. As a result, greater rescaling is required to
resurrect signal amplitudes in GRCTellus relative to GRGS and
JPL-Mascons and these scaling factors depend upon uncertain and incomplete a
priori knowledge of terrestrial water stores derived from large-scale
land-surface or hydrological models, which generally do not consider the
existence of Lake Victoria, the second largest lake by area in the world.
Descriptive statistics of various GRACE TWS signals and
statistical associations with soil moisture derived from GLDAS land-surface
models, observed surface water, and groundwater storage changes estimated
over the Lake Victoria and Lake Kyoga basins are provided in the
Supplement.
The Supplement related to this article is available online at https://doi.org/10.5194/hess-21-4533-2017-supplement.
RT conceived this study for which preliminary analyses were
carried out by DJ and MS. MS and DJ have processed GRACE and all
observational datasets and conducted statistical analyses and GIS mapping. LL
conducted the analysis of spatial leakage and bias in GRACE signals. CT, RT
and MO helped to establish, collate and analyse groundwater-level data; CT
provided dam release data. MS and RT wrote the manuscript and LL, DJ, MO and
CT commented on draft manuscripts.
The authors declare that they have no conflict of interest.
Acknowledgements
We kindly acknowledge NASA's MEaSUREs Program
(http://grace.jpl.nasa.gov) for the freely available gridded
GRCTellus and JPL-MASCON GRACE data and French National Centre for
Space Studies (CNES) for GRGS GRACE data. NASA's Precipitation Processing
Center and NASA's Hydrological Sciences Laboratory and the Goddard Earth
Sciences Data and Information Services Centre (GES DISC) are duly
acknowledged for TRMM rainfall and soil moisture data from GLDAS land-surface
models. We kindly acknowledge the Directorate of Water Resources Management
in the Ministry of Water and Environment (Uganda) for the provision of
piezometric and lake-level data. Support from the UK government's UPGro
Programme, funded by the Natural Environment Research Council (NERC),
Economic and Social Research Council (ESRC) and the Department For
International Development (DFID) through the GroFutures:
Groundwater Futures in Sub-Saharan Africa catalyst (NE/L002043/1)
and consortium (NE/M008932/1) grant awards, is gratefully acknowledged.
Edited by: Ying Fan
Reviewed by: two anonymous referees
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