ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-9-251-2017Basin-scale water-balance dataset (BSWB): an updateHirschiMartinmartin.hirschi@env.ethz.chhttps://orcid.org/0000-0001-9154-756XSeneviratneSonia I.Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 8092 Zürich, SwitzerlandMartin Hirschi (martin.hirschi@env.ethz.ch)30March20179125125821July201624October201624February201728February2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://essd.copernicus.org/articles/9/251/2017/essd-9-251-2017.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/9/251/2017/essd-9-251-2017.pdf
This paper presents an update of a
basin-scale diagnostic dataset of monthly variations in terrestrial water
storage for large river basins worldwide (BSWB v2016,
doi:10.5905/ethz-1007-82).
Terrestrial water storage comprises all forms of water storage on land
surfaces, and its seasonal and inter-annual variations are mostly determined
by soil moisture, groundwater, snow cover, and surface water. The dataset
presented is derived using a combined atmospheric and terrestrial
water-balance approach with conventional streamflow measurements and
reanalysis data of atmospheric moisture flux convergence. It extends a
previous, existing version of the dataset temporally and
spatially.
Introduction
Terrestrial water storage (TWS) plays a key role in the
hydrological cycle. It encompasses all water stored on land surfaces, and its
seasonal and inter-annual variations are determined by soil moisture,
groundwater, snow cover, and surface water. Soil moisture, especially, contributes to land–atmosphere coupling in an essential way
e.g.. In particular, it is important for numerical
weather prediction e.g. and
seasonal forecasting e.g., as well as
for simulations of present and future climate
e.g..
Despite recent activities in assembling in situ soil moisture observations
, global coverage remains limited. This is even more the
case for in situ observations of the other components of TWS. Remote sensing
can help to increase the spatial coverage with observations. For soil
moisture, the European Space Agency (ESA) Climate Change Initiative (CCI,
http://www.esa-soilmoisture-cci.org) provides a long-term global soil
moisture product from merging data from active and passive microwave sensors
. However, penetration depth is limited to the top few
centimetres of the soil . On the other hand, remote-sensing-based measurements of TWS from the Gravity Recovery and Climate Experiment
GRACE; only go back to
2002. Thus, for further retrospective evaluation of TWS, alternative
approaches are required.
Here, we rely on a combination of streamflow measurements (relatively broadly
available) and an observation-assimilating atmospheric reanalysis system to
diagnose TWS variations on a basin scale e.g.. This allows for an evaluation of TWS variations in gauged river
basins worldwide and over a longer time period (mostly limited by the
availability of streamflow data). The basin-scale water-balance
dataset of monthly TWS variations (BSWB v2016) presented here extends a previous version of
the dataset (; hereafter referred to as BSWB v2011; data available at
www.iac.ethz.ch/url/bswb) temporally and spatially.
Methodology
The method used to derive the BSWB v2016 dataset is based on publications
describing previous versions of the data . For a given river basin, the terrestrial water balance can be
expressed as
∂S∂t‾=P‾-E‾-R‾,
where S represents the TWS of the given basin, P the precipitation, E
the evapotranspiration, and R the measured streamflow, which is assumed to
include both the surface and the groundwater run-off of the area. The overbar
denotes a temporal average (i.e. monthly means) and a
space average over the basin.
Neglecting the contribution of the liquid and solid water in clouds
, the atmospheric water balance for the same area can be
expressed as
∂W∂t‾=-∇H⋅Q‾-P‾-E‾,
where W represents the column storage of water vapour and Q the
vertically integrated two-dimensional water vapour flux. The operator
(∇H⋅) denotes the horizontal divergence. Eliminating the term
P‾-E‾ in Eqs. ()
and () results in the combined atmospheric and terrestrial
water-balance equation:
∂S∂t‾=-∂W∂t‾-∇H⋅Q‾-R‾.
The monthly variations in TWS of the studied basin can thus be expressed as
the sum of three terms only: the change in atmospheric water vapour content,
the water vapour flux convergence, and the measured river streamflow. The term
∂W/∂t‾ is usually negligible
for annual means but not for monthly means, particularly during the spring
and fall seasons .
Data sourcesReanalysis data
The vertically integrated atmospheric moisture flux divergence and water
vapour content are taken from the ERA-Interim reanalysis product
of the European Centre for Medium-Range Weather Forecasts
(ECMWF).
ERA-Interim is produced with a 2006 version of the IFS (Integrated
Forecasting System, Cy31r2). It has a T255 spherical harmonic representation of the atmospheric dynamic and thermodynamic fields, corresponding to
grid spacings of about 80 km, on 60 vertical levels from the surface up to
0.1 hPa. Here, we use the interpolated 0.5×0.5∘ product. The
reanalysis covers the period from 1979 to the near present and uses a 12 h 4D-Var
assimilation technique. The two ERA-Interim fields used in the water-balance
computations (i.e. atmospheric moisture flux divergence and water vapour
content) contain assimilated humidity and wind observations from radiosondes.
Additional documentation on ERA-Interim can be found at
http://www.ecmwf.int/en/research/climate-reanalysis/era-interim.
Streamflow data and catchment boundaries
The monthly streamflow data have been obtained from the Global Runoff Data
Center (GRDC). We use the GRDC reference dataset
(http://www.bafg.de/GRDC/EN/04_spcldtbss/43_GRfN/refDataset_node.html), which compiles time series of river discharge data of 718 stations of the
GRDC database longer than 20 years, each capturing a basin area greater than
10 000 km2. The GRDC reference dataset time series are updated
regularly.
Global coverage of river basins of the BSWB v2016 dataset.
Long-term averages of
∂S/∂t‾ as indication for the
imbalance vs. domain size of the basins.
Comparison of BSWB v2011 and BSWB v2016 data of the Elbe (at Neu
Darchau) and the Rhine (at Rees) river basins (with numbers in brackets
denoting the GRDC station number). Top panels show the absolute time series and bottom panels the anomalies with
respect to the mean seasonal cycle (with the correlation between the anomaly
time series noted as well). For BSWB v2016, both the original and the
drift-corrected data are displayed for the absolute time series.
As Fig. , but for the Volga (at Volgograd power
plant) and the Columbia (at The Dalles) river basins.
Correlations between BSWB v2011 and BSWB v2016 time series
(drift-corrected data) of absolute TWS variations as well as of the anomalies
in the river basins covered by both datasets and with at least 4 overlapping years of data.
RiverAbsoluteAnomaliesLena at Stolb1.000.96Selenga at Mostovoy1.000.91Yenisey at Igarka1.000.98Irtysh at Omsk0.990.89Ob' at Salekhard0.990.91Yukon River at Pilot Station, AK0.980.85Columbia River at The Dalles, OR0.990.95Mackenzie River at Arctic Red River0.990.96Rhône at Beaucaire1.000.96Rhine River at Rees0.940.87Weser at Intschede1.000.98Elbe River at Neu Darchau0.970.93Volga at Volgograd power plant1.000.96Mean0.990.93
Catchment boundaries are provided by the GRDC as shape polygons. These are used to average the ERA-Interim fields over the basin area (see below).
BSWB v2016 datasetProcessing
The ERA-Interim atmospheric moisture flux divergence and water vapour content
are processed to monthly averages first. Then these fields are averaged over
the basin area using the fractional coverage of the catchments in each
ERA-Interim grid cell as a weighting factor. For the basin averaging, we use
the R-package “raster”
(http://cran.r-project.org/web/packages/raster/). Note that basin masks
with the fraction of the grid cell
inside the catchment are provided for different spatial resolutions as part
of the BSWB v2016 dataset (see Sect. 7).
From the GRDC reference dataset, the monthly data are used. The flags
provided are applied in the following way:
Flag 99 (use not recommended by the provider) is set to missing data.
Monthly data based on more than 10 missing daily values is set to missing
data.
GRDC provides two data streams as part of the reference dataset, “original”
data from the data provider and “calculated” data that was modified by
GRDC. As suggested by GRDC, we use the calculated values for times when they
are available, otherwise the original values are used (GRDC, personal
communication). Finally, the monthly variations in TWS are calculated using
Eq. ().
The critical domain size for water-balance computations using high-resolution
reanalysis data is assumed to be of the order of 105 km2
(). Smaller basins often suffer from
an imbalance between moisture convergence and streamflow see below,
and e.g.. For consistency with BSWB v2011, we
consider basins greater than 35 000 km2.
Moreover, only basins covering more than 6 overlapping years of streamflow
and reanalysis data are presented. Consequently, the BSWB v2016 dataset
covers the time period 1979–2015, but is often limited by availability of
streamflow data. The resulting global coverage with basins is displayed in
Fig. . Currently, the BSWB v2016 dataset encompasses 341
river basins.
Imbalance and drift correction
In the long term, the water input to a basin should be balanced by the water
output. As the contribution of the changes in column storage of water vapour
are negligible for annual to long-term means, column integrated moisture flux
convergence should be balanced by streamflow. This assumption is generally
correct for multi-year means, although some regions may show persistent
trends, e.g. due to groundwater withdrawal e.g.. Other neglected factors are inter-basin groundwater flow as well
as direct groundwater discharge to the ocean. This latter term is not
included in the measured streamflow and represents approximatively 6 % of
the total annual global water gain by the oceans .
In particular smaller river basins preferentially suffer from an imbalance
between long-term means of the vertically integrated water vapour
flux convergence and the streamflow of the
basin. Figure displays this imbalance as the long-term
average of ∂S/∂t‾ vs. the
domain size of the river basins. Our results roughly confirm the threshold
for the critical domain size for water-balance computations (105 km2,
see Sect. ) as the imbalance decreases above this basin
size.
Scatter plots of BSWB v2011 vs. BSWB v2016; all basins covered by
both datasets. (a) Absolute values of monthly TWS variations;
(b) their anomalies with respect to the mean seasonal cycle.
Box–percentile plot showing the distributions of the correlations
between BSWB v2016 and GRACE time series for absolute values and for their
anomalies with respect to the mean seasonal cycle. Basins with at least 4 overlapping years of data and with at least 9 years for the calculation of
the mean seasonal cycle are considered, with the resulting number of basins
denoted in the plot. The distributions are displayed for all basin sizes as
well as for basins larger than 105 km2. The width of the box at any
given height is proportional to the percent of observations that are more
extreme in that direction. The median and the 25th and 75th percentiles are marked
by lines across the box.
As a consequence of the imbalance, the temporal integration of the diagnosed monthly variations in TWS, i.e
S(t)=S0+∫t0t∂S∂t‾dtwithS0=0mm,
shows a drift in TWS. The likely reason for this drift are biases in the
atmospheric moisture convergence data , though actual
drifts in TWS can be important in some regions and could contribute to part
of the signal (see above). The errors in the run-off measurements are expected
to be small, i.e. around 5 % for longer-term averages. As
widespread information on natural sources of drifts in TWS is not available,
the most appropriate procedure is to assume that the observed drifts are
purely artificial and to remove them by a high-pass filter.
The drift correction is achieved by subtracting a running mean with a 3-year
window from the original estimates of TWS variations. This forces the
long-term average of ∂S/∂t‾ to
zero (cf. red dots in Fig. ) and allows the removal of the
artificial drift without losing the short-term variability. Note that a
remaining imbalance might persist on Fig. due to
non-complete years of data.
We provide both the original and the drift-corrected estimates as part of the
BSWB v2016 dataset. In this way, the application of alternative filters (e.g.
locally weighted regression LOESS) to the original estimates is still
possible for the user. Note that due to the reasons presented, the BSWB data
are not applicable in trend analyses.
Comparison with previous BSWB v2011
Due to the availability of ERA-Interim and streamflow data, BSWB v2011 was
restricted to the 1989–2008 time period. ERA-Interim has been updated and
temporally extended since then (see
http://www.ecmwf.int/en/about/media-centre/news/2011/extension-era-interim-reanalysis-1979).
In addition, the BSWB v2016 datasets relies on a consistent run-off database
from GRDC (i.e. the GRDC reference dataset), while BSWB v2011 was based on
various heterogeneous data sources for run-off (apart from GRDC also the US Geological Survey and local sources with varying data formatting and quality
checks). By using the GRDC reference dataset, enhanced consistency between
the basins can be ensured, and future updates of the BSWB dataset are more
easily feasible by relying on one source for run-off only.
To check the consistency of the updated BSWB v2016 dataset with the previous
BSWB v2011, we compare time series of both versions for some river basins.
Note that despite the varying data sources (see above), the differences
between the two dataset versions should only be minor.
Figures and show time series of the BSWB
v2011 and BSWB v2016 datasets for selected basins. The datasets agree very
well, both for the absolute TWS variations as well as for their anomalies
(i.e. anomalies with respect to the mean seasonal cycle). Correlations
amount to 0.99 on the average for absolute TWS variations and are mostly
higher than 0.85 for the anomalies (for basins with at least 4 overlapping
years of data, see Table ). This good agreement is also visible
in the scatter plots based on all basins covered by both datasets
(Fig. ), which again show high correlations between
BSWB v2011 and BSWB v2016 both for absolute values and the anomalies. Despite
this close agreement, differences between BSWB v2011 and BSWB v2016 exist and
are likely related to changes in ERA-Interim. For instance the previously
existing negative outlier in Europe in 2003 was caused by a strong moisture
divergence which has been alleviated in the extended ERA-Interim version (see
Fig. ).
Comparison with GRACE
The BSWB v2016 dataset is also compared with independent remote-sensing-based
estimates of TWS from GRACE . We use the Jet Propulsion
Laboratory (JPL)-RL05M GRACE mascon (mass concentration blocks) solution, providing equivalent water thickness with a spatial sampling of
0.5×0.5∘doi:10.5067/TEMSC-OCL05;.
The BSWB v2016 drift-corrected monthly variations in TWS have been temporally
integrated for the comparison with GRACE (see also
Sect. ).
Figure displays the distributions of the time series
correlations between BSWB v2016 and GRACE for the absolute values as well as
for their anomalies (i.e. anomalies with respect to the mean seasonal cycle)
as box–percentile plots . Basins with at least 4 overlapping years of data and with at least 9 years for the calculation of
the mean seasonal cycle are considered. The analysis is done separately for
all basin sizes as well as for basins larger than 105 km2. The two
datasets mostly agree well, with slightly higher correlations for the larger
basins. Mean correlations amount to 0.7 for the absolute values and 0.51 for
the anomalies.
The basin-scale diagnostic
dataset of monthly variations in terrestrial water storage (BSWB v2016) presented here is
available for download at the ETH data archive:
doi:10.5905/ethz-1007-82. The
data are provided in individual ASCII files for each of the river basins and
contain time series of the uncorrected monthly variations in terrestrial
water storage (in units of mm d-1) as well as the drift-corrected data
(see Sect. ). Moreover, gridded masks of the river
basins are provided to facilitate comparison with other gridded data (e.g.
climate model output). The masks are made available in NetCDF format for
three spatial resolutions (1×1, 0.5×0.5, and
0.25×0.25∘) and contain the fraction of the grid cell inside the
respective catchment. Older versions of the BSWB dataset are available at the
following website: www.iac.ethz.ch/url/bswb.
Conclusions
We present an update of a basin-scale diagnostic dataset of
monthly variations in terrestrial water storage for large river basins
worldwide (BSWB v2016). The dataset is derived using a combined atmospheric
and terrestrial water-balance approach with conventional streamflow
measurements and atmospheric reanalysis data from ECMWF ERA-Interim. It
extends the existing version of the dataset temporally
and spatially (i.e. 1979–2015 vs. 1989–2008, 341 vs. 36 river basins).
Overall, the update shows very good agreement with the previous version of
the dataset. It also compares well with independent remote-sensing-based
estimates of TWS. BSWB data proved to be valuable for climate and
land-surface model evaluation e.g. and the investigation of land-surface processes
e.g., as well as for the evaluation of other
large-scale estimates of TWS e.g..
The authors declare that they have no conflict of
interest.
Acknowledgements
We thank ECMWF for providing the atmospheric reanalysis data and GRDC for the
run-off data. GRACE land mass grids were processed by the Jet Propulsion
Laboratory (JPL) GRACE Team and are available at
http://grace.jpl.nasa.gov, supported by the NASA MEaSUREs Program. We
thank Vincent Humphrey for help with the GRACE data. Partial support from the
ERC DROUGHT-HEAT project (grant agreement FP7-IDEAS-ERC-617518) is
acknowledged.Edited by: D. Carlson
Reviewed by: two anonymous referees
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