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Joint atmospheric-terrestrial water balances for East Africa: a WRF-Hydro case study for the upper Tana River basin


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For an improved understanding of the hydrometeorological conditions of the Tana River basin of Kenya, East Africa, its joint atmospheric-terrestrial water balances are investigated. This is achieved through the application of the Weather Research and Forecasting (WRF) and the fully coupled WRF-Hydro modeling system over the Mathioya-Sagana subcatchment (3279 km²) and its surroundings in the upper Tana River basin for 4 years (2011–2014). The model setup consists of an outer domain at 25 km (East Africa) and an inner one at 5-km (Mathioya-Sagana subcatchment) horizontal resolution. The WRF-Hydro inner domain is enhanced with hydrological routing at 500-m horizontal resolution. The results from the fully coupled modeling system are compared to those of the WRF-only model. The coupled WRF-Hydro slightly reduces precipitation, evapotranspiration, and the soil water storage but increases runoff. The total precipitation from March to May and October to December for WRF-only (974 mm/year) and coupled WRF-Hydro (940 mm/year) is closer to that derived from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data (989 mm/year) than from the TRMM (795 mm/year) precipitation product. The coupled WRF-Hydro-accumulated discharge (323 mm/year) is close to that observed (333 mm/year). However, the coupled WRF-Hydro underestimates the observed peak flows registering low but acceptable NSE (0.02) and RSR (0.99) at daily time step. The precipitation recycling and efficiency measures between WRF-only and coupled WRF-Hydro are very close and small. This suggests that most of precipitation in the region comes from moisture advection from the outside of the analysis domain, indicating a minor impact of potential land-precipitation feedback mechanisms in this case. The coupled WRF-Hydro nonetheless serves as a tool in quantifying the atmospheric-terrestrial water balance in this region.
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Joint atmospheric-terrestrial water balances for East Africa:
a WRF-Hydro case study for the upper Tana River basin
Noah Kerandi
&Joel Arnault
&Patrick Laux
&Sven Wagner
Johnson Kitheka
&Harald Kunstmann
Received: 11 August 2016 /Accepted: 17 January 2017 /Published online: 3 February 2017
#The Author(s) 2017. This article is published with open access at
Abstract For an improved understanding of the hydromete-
orological conditions of the Tana River basin of Kenya, East
Africa, its joint atmospheric-terrestrial water balances are in-
vestigated. This is achieved through the application of the
Weather Research and Forecasting (WRF) and the fully
coupled WRF-Hydro modeling system over the Mathioya-
Sagana subcatchment (3279 km
) and its surroundings in the
upper Tana River basin for 4 years (20112014). The model
setup consists of an outer domain at 25 km (East Africa) and
an inner one at 5-km (Mathioya-Sagana subcatchment) hori-
zontal resolution. The WRF-Hydro inner domain is enhanced
with hydrological routing at 500-m horizontal resolution. The
results from the fully coupled modeling system are compared
to those of the WRF-only model. The coupled WRF-Hydro
slightly reduces precipitation, evapotranspiration, and the soil
water storage but increases runoff.The total precipitation from
March to May and October to December for WRF-only
(974 mm/year) and coupled WRF-Hydro (940 mm/year) is
closer to that derived from the Climate Hazards Group
Infrared Precipitation with Stations (CHIRPS) data
(989 mm/year) than from the TRMM (795 mm/year) precip-
itation product. The coupled WRF-Hydro-accumulated dis-
charge (323 mm/year) is close to that observed (333 mm/
year). However, the coupled WRF-Hydro underestimates the
observed peak flows registering low but acceptable NSE
(0.02) and RSR (0.99) at daily time step. The precipitation
recycling and efficiency measures between WRF-only and
coupled WRF-Hydro are very close and small. This suggests
that most of precipitation in the region comes from moisture
advection from the outside of the analysis domain, indicating
a minor impact of potential land-precipitation feedback mech-
anisms in this case. The coupled WRF-Hydro nonetheless
serves as a tool in quantifying the atmospheric-terrestrial wa-
ter balance in this region.
1 Introduction
Kenya, East Africa, is classified as a water-scarce nation
(Krhoda 2006). This situation is likely to continue in the near
future (Williams and Funk 2011), although there are also in-
dications that precipitation may slightly increase (Niang et al.
2014). In a future climate projection study, Nakaegawa and
Wachana (2012) found an increase of all the four components
of the terrestrial water balance, i.e., precipitation, evapotrans-
piration, water storage, and runoff, for the particular case of
the Tana River basin (TRB), Kenya. This uncertainty in
Kenyan precipitation calls for improved monitoring of water
resources in this region. Precipitation is considered the most
critical of all the hydrometeorological variables in Kenya and
East Africa in general (e.g., Endris et al. 2013). However, all
the other hydrometeorogical variables are equally important as
they contribute to the water resources in a given region. This
calls for a comprehensive investigation of all these variables.
Steps towards this direction are significant as water in its en-
tirety is utilized in several sectors that include agriculture,
hydropower, domestic, industrial, and ecological maintenance
(Agwata 2005). One way to achieve this is to investigate the
*Noah Kerandi
Kalrsruhe Institute of Technology, Campus Alpin, Institute of
Meteorology and Climate Research (IMK-IFU),
Kreuzeckbahnstrasse 19, 82467 Garmisch-Partenkirchen, Germany
Institute of Geography, University of Augsburg, Alter Postweg 118,
86135 Augsburg, Germany
Institute of Mineral Processing and Mining, South Eastern Kenya
University, P.O. Box 170-90200, Kitui, Kenya
Theor Appl Climatol (2018) 131:13371355
DOI 10.1007/s00704-017-2050-8
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
joint atmospheric-terrestrial water balance, which relates the
atmospheric moisture flow to precipitation, evapotranspira-
tion, water storage, and runoff (Eltahir and Bras 1996). Such
atmospheric-terrestrial water balance studies take care of the
entire regional water cycle, which is understood to be
interlinked in a complex way. For instance, changes in soil
moisture condition can be related to changes in precipitation
through land-atmosphere feedback mechanisms (Kunstmann
and Jung 2007). A better knowledge of the atmospheric-
terrestrial water balance will provide vital hydrometeorologi-
cal information related to water resources in the considered
region. We can gain this knowledge through the application of
the coupled atmospheric-hydrological modeling system.
Unfortunately, most studies on water balance are skewed to-
wards the terrestrial branch (Eltahir and Bras 1996). Yet the
operational nature of the water cycle in its entirety involves the
terrestrial and atmospheric branches. Recognizing their
coupled roles is essential in the rational application of the
whole water cycle (Shelton 2009). The coupled
atmospheric-hydrological modeling is considered a novel de-
velopment that is a means to achieve the aforementioned. The
main objective of this study, therefore, is to contribute to a
better understanding of the hydrometeorology of the TRB.
In particular, our study investigates the impact of the coupled
atmospheric-hydrometeorological modeling system compared
to only atmospheric modeling system. Our investigation will
be focused on the atmospheric-terrestrial water balance
Changes in soil moisture, i.e., water storage, are considered
to be of great importance for water resources, climate, agricul-
ture, and ecosystems (Yeh and Famiglietti 2008). A number of
studies (e.g., Findell and Eltahir 2003; Koster et al. 2004;
Anyah et al. 2008) have argued that the influence of local soil
moisture changes on precipitation is largest in arid and semi-
arid regions dominated by convective precipitation, like Kenya.
These soil moisture-precipitation interactions have been stud-
ied with the concepts of precipitation recycling ratio and pre-
cipitation efficiency (Eltahir and Bras 1996; Schär et al. 1999;
Kunstmann and Jung 2007), which emphasize the significance
of evapotranspiration on local precipitation. At river basin
scale, both advection and evapotranspiration contribute to pre-
cipitation (Trenberth 1999). The precipitation recycling analy-
sis allows the quantification of the interaction between the at-
mospheric and terrestrial water balance components.
Studies investigating these interactions are few in most
regions primarily due to lack of in situ observations of hydro-
meteorological data such as humidity, wind, radiation, air
pressure, soil moisture, evapotranspiration, and runoff.
Kenya and East Africa in general are among these regions.
The lack of data can be mitigated by the use of regional cli-
mate model (RCM) data for atmospheric-terrestrial water bal-
ance study (e.g., Kunstmann and Jung 2007; Music and Caya
2007; Roberts and Snelgrove 2015).
As stated by Kunstmann and Stadler (2005), the applica-
tion of RCMs coupled with hydrological models is gaining
scientific attention as it enhances the description of soil pro-
cesses involved in the terrestrial water balance. The coupling
can be said to take advantage of the nesting capabilities of the
atmospheric model, which can be nested into a global model
to allow large-scale integration (Bronstert et al. 2005). The
coupling of atmospheric and hydrological models can be
achieved through one-way, two-way, or integrated
(integrative) modeling (Bronstert et al. 2005). The one-way
coupling is the simplest way, in which the coupling drives the
hydrological models by outputs of atmospheric models. Both
hydrological and atmospheric models describe the same land
surface processes, but the modeling system does not allow
feedback between the two (Zabel and Mauser 2013). In a
two-way coupling, the feedback is allowed, which leads to
production of subgrid scale land surface fluxes and generally
an improvement of model simulations (Zabel and Mauser
2013). It is argued that the coupled modeling approach has
the advantage of including the soil moisture redistribution
feedback in the lower boundary conditions of atmospheric
models, which may lead to an improved representation of
water and energy fluxes between land and atmosphere
(Maxwell et al. 2011; Shrestha et al. 2014; Senatore et al.
2015; Arnault et al. 2016;Wagneretal.2016). Maxwell
et al. (2007) showed that the fully coupled modeling system
yields a topographically driven soil moisture distribution and
depicts a spatial and temporal correlations between surface
and lower atmospheric variables and water depth. This may
suggest that the fully coupled models are regulated by the
geographical location of the area under study.
The coupledWRF-Hydro, a combination of the atmospher-
ic Weather Research and Forecasting (WRF) model and a
hydrological module referred to as uncoupled WRF-Hydro
(Skamarock et al. 2008;Gochisetal.2015), provides such a
coupling approach. This coupled modeling system is a recent
development designed to provide more accurate information
related to the spatial redistribution of surface, subsurface, and
channel waters across land surfaces and more importantly as
an enhancement to coupling of hydrologic models with atmo-
spheric models. Both coupled and uncoupled WRF-Hydro
systems have been applied for studies in a number of places
in the world (e.g., Yucel et al. 2015; Senatore et al. 2015;
Arnault et al. 2016). Yucel et al. (2015) applied the model in
uncoupled mode to evaluate flood forecasting over mountain-
ous basins in the western Black Sea region of Turkey. They
found the model to reasonably simulate many important fea-
tures of flood events in the area. Senatore et al. (2015)usedthe
WRF-Hydro in coupled mode over the Crati River basin,
Southern Italy, and found that the coupled model showed bet-
ter results in simulation of the water cycle components than
the atmospheric model in stand-alone mode (WRF-only).
Recently, Arnault et al. (2016) applied the coupled WRF-
1338 N. Kerandi et al.
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Hydro for investigating the role of runoff-infiltration
partitioning and resolved overland flow on land-atmosphere
feedback mechanisms over West Africa and postulated the
potential of such coupled modeling system in application for
joint atmospheric-terrestrial water balance studies.
These previous studies suggest that coupled atmospheric-
hydrological modeling significantly affects the simulated
atmospheric-terrestrial water balance (Maxwell et al. 2011;
Senatore et al. 2015; Arnault et al. 2016;Wagneretal.
2016), especially in arid and semi-arid regions where soil
moisture-precipitation interactions are largest (e.g., Findell
and Eltahir 2003;Kosteretal.2004; Anyah et al. 2008). It is
against this background that this study applies the coupled
WRF-Hydro modeling system for the Mathioya-Sagana
subcatchment (MSS) in the upper TRB, Kenya. The study
region has been chosen for its location, i.e., upstream of the
Masinga dam, the availability of discharge data, and its crucial
role in contributing to the agricultural sector of Kenyasecon-
omy. The WRF-only and coupled WRF-Hydro models are
applied to MSS for a 4-year period (2011 to 2014). Model
results are used to investigate the atmospheric-terrestrial water
balance and precipitation recycling over the region. The im-
pact of the enhanced description of hydrological processes in
WRF-Hydro is investigated by comparing WRF-Hydro and
WRF results with observations.
Section 2provides a brief description of the study area,
models, experimental design, data, and methodology.
Results are given in Sect. 3, followed by a summary and
conclusion of our results in Sect. 4.
2 Study area, models, data, and methodology
2.1 The study area
The Mathioya-Sagana subcatchment (MSS) is a portion up-
permost of the upper Tana River basin(TRB)catchment. More
specifically, it lies between 0° 10and 0° 48S and 36° 36
and 37° 18E(Fig.1a; see the red contour boundary) covering
an area of approximately 3279 km
(20.5% of the entire
upper TRB). The upper TRB is about 16,000 km
2010) with elevation of between 400 m a.s.l. (on the eastern
part of the catchment) and 5199 m.a.s.l. on Mount Kenya
(Geertsma et al. 2009). The MSS, in particular, has an eleva-
tion of between 1000 and 4700 m a.s.l. It is served by a num-
ber of tributaries most of them perennial that include Sagana,
Ragati, New Chania, Amboni, Mathioya, Gura, Gakira, and
Rukanga. All these tributaries are part of the Tana River drain-
age network that has its source at the slopes of Mount Kenya
and the Aberdare Ranges. Tana River is the longest river in
Kenya stretching about 1012 km with an annual mean dis-
charge of 5 × 10
(Agwata 2005). The river network of
the MSS contributes remarkably to the Tana River network.
This is because these rivers are upstream of the entire Tana
River network. Besides, they are just in the vicinity of the
sources of Tana River itself, i.e., Mt. Kenya and the
Aberdare Ranges. The Rukanga River is most downstream
of all these tributaries with the river gauge station (RGS
4BE10; 0° 4353S, 37° 1529E) located at the outlet of
MSS. The Tana Rukangas RGS 4BE10 discharge is used for
calibration and evaluation of the relevant model in this study.
The study area (MSS and the surrounding area; Fig. 1), like
most parts of East Africa, receives its rainfall in two seasons
during March, April, and May (MAM) and October,
November, and December (OND) locally known as the Blong
rains^and Bshort rains,^respectively, due to the south-north
oscillation of the Intertropical Convergence Zone (ITCZ)
(Kitheka et al. 2005; Nakaegawa and Wachana 2012;
Oludhe et al. 2013). The mean annual rainfall ranges between
960 and 1200 mm, while climatologically, the region experi-
ences low annual/monthly mean temperatures of about 17 °C
or less (Kerandi et al. 2016).AccordingtotheModerate
Resolution Imaging Spectroradiometer (MODIS, 20 classes;
Friedl et al. 2002) based land use classification, the dominant
land use classes are the evergreen broadleaf forest and the
savannas and woody savannas (Fig. 1b).
2.2 Model description and the experimental design
The fully coupled modeling system used in this study consists
of two models, the Weather Research and Forecasting (WRF)
model whose details are described by Skamarock et al. (2008;
details are also available online at
and its hydrological extension package referred to as WRF-
Hydro (Gochis et al. 2015; details can also be found online at The WRF
model is a non-hydrostatic, mesoscale Numerical Weather
Prediction (NWP) and atmospheric simulation system. It is
designed with a flexible code and offers several physical op-
tions (parameterizations) to choose from. In addition, the
WRF-Hydro facilitates coupling of multiple hydrological pro-
cess representation together. It is purposed to account for land
surface states and fluxes and provides physically consistent
land surface fluxes and stream channel discharge information
for hydrometeorological applications. A brief overview of the
experimental design of these two models and the coupling
process is discussed in Sects. 2.2.1 and 2.2.2.
2.2.1 Weather Research and Forecasting model
In this study, WRF version 3.5.1 was used for all experiments.
Details of the WRF physics schemes and experimental details
for this study are shown in Table 1and explained in more
details by Kerandi et al. (2016).
Two one-way nest domains with the larger domain, D1 at
25-km and D2 at 5-km horizontal resolution, are considered
Joint atmospheric-terrestrial water balances for East Africa 1339
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for this study. D1 is defined with 140 × 120 grid points east-
west and north-south directions, respectively, and extends 12°
S13° N; 22°53° E. D2 is defined with 121 × 121 grid points
covering 3° 342S2° 1718N; 34° 3343″–39° 5450
E encompassing the whole of upper TRB (Fig. 2). D2 is ad-
ditionally coupled with routing process at 500-m resolution
with 1200 × 1200 grid points east-west and north-south direc-
tions, respectively. The fully coupled simulations together
with the routing processes (explained in Sect. 2.2.2) are based
on D2.
The simulations are initialized on November 1, 2010, in-
cluding a spin-up period of 2 months and cover a 4-year peri-
od from 2011 to 2014. The model domains use 40 vertical
levels up to 20 hPa (approximately 26-km vertical height
above the surface). ERA-Interim reanalysis (Dee et al. 2011)
provides the initial and lateral boundary conditions for the
2.2.2 Weather Research and Forecasting-Hydro
The WRF-Hydro model permits a physics-based, fully
coupled land surface hydrology-regional atmospheric model-
ing capability for use in hydrometeorological and
hydroclimatological research applications (Gochis et al.
2015). The model can be used both in an uncoupled (stand-
alone or offline) mode and in a coupled mode to an atmospher-
ic model and other Earth System modeling architectures.
In uncoupled mode, its land surface model (in our case, the
Noah land surface model (Noah LSM)) acts like any land
surface hydrological modeling system. It requires meteorolog-
ical forcing data prepared externally and provided as gridded
data. This is the uncoupled WRF-Hydro used for the calibra-
tion in Sect. 3.1. Otherwise, the enhanced description of hy-
drological processes in uncoupled WRF-Hydro is the same as
that in the coupled mode.
Fig. 1 a Map of study area and the location of one meteorological station
(Nyeri), two rain stations ( Sagana, Muranga), and one discharge gauge
(black triangle;TanaRukangas RGS 4BE10). Red contour marks the
Mathioya-Sagana subcatchment (MSS) in the northwest of the upper
Tana River basin (TRB), Kenya. Also shown is the digital elevation
model (DEM; derived from the 3(90 m) USGS HydroSHEDS at 500-
m resolution) and river network in the study area (b) dominant land use
categories in the study area based on the Moderate Resolution Imaging
Spectroradiometer (MODIS) at 30resolution. Map of Africa (top left)is
processed from Natural Earth data;
1340 N. Kerandi et al.
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In its coupled mode, WRF-Hydro generally leads to an
improved simulation of the full regional water cycle with
its capability of permitting atmospheric, land surface, and
hydrological processes from available physics options. Such
options are referred to as routing processes and include
surface overland, subsurface, channel, and conceptual
baseflow (bucket model). The routing time step is set in
accordance with the routing grid spacing (Gochis et al.
2015). In this study, all these routing processes have been
activated and hence contribute to the simulated discharge.
Four soil layers are used: 010, 1040, 40100, and 100
200 cm. In this mode, the WRF model provides the re-
quired meteorological forcing data with a frequency dictat-
ed by the Noah LSM time step specified for D2 (in our
case, 20s). This enhances the interaction between the hydro
model components with the Noah LSM and WRF model
physics. Specific details relevant to the WRF-Hydro are
provided in Table 2.
Fig. 2 Map of East Africa
showing the location of model
domains at 25- and 5-km hori-
zontal resolution (D1 in black and
D2 in pink box, respectively). D1
is defined by 140 × 120 grid
points and extends 12° S13° N;
22°53° E, and D2 is defined by
121 × 121 grid points covering 3°
342S2° 1718N; 34° 33
43″–39° 5450E encompassing
the whole of upper TRB (inset red
contour). Blue contour shows the
boundary of the entire TRB
Table 1 The experimental details
of the atmospheric model, WRF Subject Chosen option Reference
Driving data ERA-Interim Dee et al. (2011)
Horizontal resolution 25 km, 5 km
Horizontal grid number 140 × 120, 121 × 121
Integration time-step 100 s for D1
Projection resolution Mercator
Simulation period November 1, 2010, to December 31, 2012
Vertical discretization 40 layers
Pressure top 20 hPa
WRF output interval 24 h
Cumulus convection Kain-Fritsch (KF) Kain (2004)
Microphysics scheme WRF Single-Moment 6-class (WSM6) Hong et al. (2006)
Planetary boundary layer Asymmetric Convection Model (ACM2) Pleim (2007)
Longwave radiation New Goddard Chou and Suarez (1999)
Shortwave radiation
Land surface scheme Noah LSM Chen and Dudhia (2001)
Land use MODIS Friedl et al. (2002)
Surface layer MM5 similarity Monin and Obukhov (1954)
Joint atmospheric-terrestrial water balances for East Africa 1341
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In coupled WRF-Hydro, the hydrological component is
called directly from WRF in the WRF surface driver module.
This is accomplished at the coupling interface by the WRF-
Hydro coupling interface module. The interface serves to pass
data, grid, and time information between WRF and WRF-
Hydro. The WRF-Hydro components map data and subcom-
ponent routing processes (e.g., land and channel routing).
Upon completion of these processes, the data is remapped
back to the WRF model (by the WRF-Hydro driver) through
the coupling interface. The details of these routing processes
are available in literature (e.g., Gochis et al. 2015;Senatore
et al. 2015).
2.3 Observational and gridded datasets
2.3.1 Precipitation and discharge
The satellite estimates of the Tropical Rainfall Measuring
Mission (TRMM, 3B42 v7 derived daily at 0.25° horizontal
resolution, 1998 to 2015; Huffman et al. 2007), the station
rainfall, and discharge from the Tana RukangasRGS
4BE10 are used. In addition, the Climate Hazards Group
Infrared Precipitation with Stations (CHIRPS; CHIRPS
v2.0 at 0.05° horizontal resolution; 1981near present; Funk
et al. 2015), a recent global dataset available to the public, is
used. Like TRMM, it has a spatial coverage spanning 50° S
50° N (and all longitudes). CHIRPS dataset is based on satel-
lite imagery with in situ station data, and it provides a daily
resolution. It is designed as a suitable alternative for data-
sparse regions characterized by convective rainfall. Details
on CHIRPS can be found at http://chg.geog.ucsb.
Figure 3shows the mean annual evolution of monthly pre-
cipitation (based on CHIRPS, TRMM, and station rainfall)
and discharge averaged for 2011 to 2014. The subcatchment,
like the rest of the TRB, experiences bimodal precipitation and
discharge patterns (Maingi and Marsh 2002; Oludhe et al.
2013). It is observed that the peak month for the rains over
MSS occurs during April and November. In the case of the
MAM season, the peak flows occur 1 month later than that of
precipitation, i.e., May, while it is in agreement during the
OND season.
The seasonality of stream flow is largely influenced by
precipitation over the subcatchment. In terms of the annual
cycle of discharge, there is a closer agreement with both
CHIRPS and TRMM datasets than gauge rainfall. With the
significance threshold set at 0.05 here and in all tests in this
study, the monthly time series for CHIRPS and TRMM have a
significant relationship with discharge [correlation coefficient
r(44) = 0.72 and r(44) = 0.75, p< 0.001, respectively]. Here,
the number in parentheses shows the Bdegrees of freedom^
defined by n2whereBn^is the number of data (sample
size). The measured discharge that is observed and recorded at
the Tana Rukanga RGS 4BE10 corresponds to only
46 months. The rainfall from the gauge manages a corre-
sponding significant relationship with discharge of r
(44) = 0.57 and p< 0.001. This could be attributed to the
coverage of the gauge rainfall, which comes from only three
stations and which is not fully representative for the whole
subcatchment unlike CHIRPS and TRMM data, which takes
averaged values over the whole subcatchment. In general,
however, the gauge rainfall and TRMM over MSS agree very
closely in terms of their annual and interannual variability
consistent with previous studies (e.g., Kerandi et al. 2016).
This is also the case with CHIRPS datasets compared with
the gauge rainfall (r(44) = 0.92, p< 0.001). In general, there
is a reasonable agreement with the gauge data and that of
TRMM and CHIRPS as seen from the amount of precipitation
that each yields based on the average rainfall from the three
stations of Nyeri, Sagana, and Murangafor4years,i.e.,
gauge rainfall of 1086 mm/year, TRMM with 1085 mm/year,
and CHIRPS with 1124 mm/year. On the other hand, TRMM
andCHIRPSarecorrelatedverywell(r(44) = 0.94,
p< 0.001). Therefore, depending on the purpose, any of these
gridded datasets can substitute the station data.
2.4 Water balance computation: theory
The water balance refers to a conceptualstructure supporting a
quantitative assessment of moisture supply and demand rela-
tionships at the land-atmosphere interface on a daily, weekly,
monthly, or annual basis (Shelton 2009). This gives rise to
what is commonly referred to as the terrestrial and atmospher-
ic branches of the water balance. At the land-atmosphere in-
terface, the loss or Boutput^of water from the earthssurface
through evaporation and evapotranspiration is the input for the
atmospheric branch, whereas for precipitation, the atmospher-
ic output is considered an input or the gain of the terrestrial
branch as in Peixoto and Oort (1992). Details of the water
balance computation are available in many textbooks as in
Table 2 The experimental details specific to uncoupled/coupled WRF-
Subject Chosen option
Nest identifier 2
Hydro output interval 360 min (6 h)
Model subgrid size (routing grid space) 500 m
Integer divisor (aggregation factor) 10
Routing model time step 20 s
Physics options/parameterizations
Subsurface routing Yes
Overland flow routing Yes
Channel routing Yes with steepest descent
Baseflow bucket model Yes with pass-through
1342 N. Kerandi et al.
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Peixoto and Oort (1992). In this section, a brief account of the
relationship of terrestrial and atmospheric water balance com-
ponents is provided.
2.4.1 Terrestrial water balance computation
The terrestrial water balance (TWB) can be written as follows:
dt ¼Rin Rout ET þP
where dS
dt is the total change of terrestrial water storage (S in
mm), Rin and Rout are the inflow and outflow of surface
runoff, respectively, which constitute total runoff R(mm/
day) (according to Oki et al. 1995,Ris simply outflow
minus inflow), ET (mm/day) is the evapotranspiration, and
P(mm/day) is the precipitation. It is noted that in Eq. 1,each
term is spatially averaged over the study area that encom-
passes the MSS.
2.4.2 Atmospheric water balance computation
The atmospheric water balance (AWB) components are relat-
ed as follows:
¼−∇⋅ Q
where dW
dt is the total change in precipitable water or atmo-
spheric storage water (W in mm), IN and OUT are the lateral
inflow and lateral outflow of water vapor flux across the lat-
eral boundaries of the MSS, respectively, and −∇⋅ Q
OUT is the mean convergence of lateral atmospheric vapor
flux in millimeter per day. The atmospheric vapor flux is com-
puted from vertically integrated moisture fluxes taking note on
the horizontal water vapor fluxes, specific humidity winds
(meridional and zonal), and surface pressure. Related
explanation of this calculation is available in the work of
Roberts and Snelgrove (2015). εis the AWB residue or im-
balance. The imbalance arises since Numerical Weather
Prediction (NWP)-derived balances do not close (Draper and
Mills 2008). Schär et al. (1999) noted that εcan be distributed
equally among the atmospheric fluxes, i.e., IN
= OUT + ε/2 in order for the atmospheric fluxes
to satisfy the balance constraints.
−∇⋅ Q
!corr ¼INcorrOUTcorr ð3Þ
where the superscript Bcorr^means corrected fluxes.
We denote C¼−∇⋅ Q
!corr (e.g., Yeh et al. 1998) in all sub-
sequent discussions.
Equation ((2))thusbecomes
dt ¼CþETPð4Þ
Two AWB measures that quantify the land-atmospheric
interactions, relating P, ET, and IN, are the recycling ratio β
and the precipitation efficiency χ. They are defined here as
derived by Schär et al. (1999) and mentioned in, e.g.,
Kunstmann and Jung (2007) and Asharaf et al. (2012)as
IN þET ð5Þ
IN þET ð6Þ
βis the precipitation recycling ratio which refers to the fraction
of precipitation in the study area that originates from evapotrans-
piration from the study area. χis the precipitation efficiency
which refers to the fraction of water that enters our study area
(either by evapotranspiration or by atmospheric transport) and
subsequently falls as precipitation. All the accompanying as-
sumptions of these two measures otherwise referred to as bulk
Fig. 3 Mean annual cycle of
monthly precipitation and
discharge averaged for the period
2011 to 2014 at the locations of
the stations (Nyeri, Sagana,
Muranga for precipitation; Tana
Rukangas RGS 4BE10 for
discharge). TRMM- and
CHIRPS-derived precipitation at
the locations of the stations is also
Joint atmospheric-terrestrial water balances for East Africa 1343
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characteristics are considered to hold true for our analysis do-
main. As in the TWB components, all terms in AWB are spa-
tially averaged over the study area that encompasses MSS.
3 Results and discussion
3.1 Calibration of the uncoupled Weather Research
and Forecasting-Hydro
The uncoupled WRF-Hydro model consists of many parameters
associated with large uncertainties. This may warrant for its
calibration before application and analysis of its hydrological
performance (Gochis et al. 2015). The meteorological forcing
data to drive the uncoupled WRF-Hydro in calibration, for in-
stance, included hourly incoming shortwave radiation
(SWDOWN) and longwave radiation (LDOWN) measured in
watt per square meter, specific humidity at 2-m height (Q2D) in
kilogram per kilogram, air temperature at 2-m height (T2D) in
Kelvin, surface pressure (PSFC) in Pascal, and near surface
winds at 10-m height: u (U2D) and v (V2D) in meter per second.
These datasets were extracted from WRF model output. The
precipitation (RAINRATE) in millimeter per second was pre-
pared from TRMM 3-hourly precipitation dataset. This was
achieved through netcdf and climate data operator (NCO and
CDO) algorithms.
The simulated discharge from the uncoupled WRF-Hydro
model for the year 2012 is compared to that recorded at Tana
Rukangas RGS 4BE10. The year 2012 was chosen as it had a
full record of discharge data. The year 2012 also showed a
normal distribution of both seasonal and annual cycles of dis-
charge more than all the other available years. One-year cali-
bration is considered reasonably long enough to evaluate the
basic parameter sensitivities.
Calibration procedure is motivated by the work of Yucel
et al. (2015) who recommended a stepwise approach for this
model to minimize the number of model runs and cut down
excessive computational time. In the present study, four pa-
rameters are considered for calibration: the surface runoff pa-
rameter (REFKDT), surface retention depth scaling parameter
(RETDEPRT), and overland flow roughness scaling parame-
ter (OVROUGHRT) from the high-resolution terrain grid and
the channel Manning roughness coefficient (MANN).
The REFKDT whose feasible range is 0.110 with default
value 3.0 controls the hydrograph volume. In our case, we
considered the range 1.06.0. The RETDEPRTFAC whose
default value is 1.0 has a similar function as REFKDT. We
considered for our calibration the range 0.05.0. The
OVROUGHRT and MANN control the hydrograph shape
(Yucel et al. 2015). In our calibration, we took the ranges
0.01.0 and 0.42.0, respectively, for these two parameters.
In the aforementioned order, the best value of REFKDT pa-
rameter obtained is fixed, while the RETDEPRTFAC is
calibrated. The best values obtained at the two first steps are
fixed in the subsequent calibration ofthe OVROUGHRTFAC.
The obtained best values for the REFKDT, the
RETDEPRTFAC, and the OVROUGHRTFAC are fixed in
the calibration of the MANN. The best value for the MANN
forms the end of our stepwise approach.
Table 3shows the calibration results based on the selected
objective criteria, i.e., the Nash-Sutcliffe efficiency (NSE) and
the RMSE observation standard deviation ratio (RSR), between
simulated and observed discharges at daily resolution for the
entire year. Values of NSE are known to range between −∞ and
1.0 (1 inclusive) with those between 0.0 and 1.0 considered
acceptable (Moriasi et al. 2007). Lower RSR values mean
low RMSE and, thus, better model simulation performance.
Figure 4summarizes the calibration results, which show that
the uncoupled WRF-Hydro reasonably reproduces the ob-
served hydrograph over this catchment. In the overall calibra-
tion period, we got a NSE and RSR of 0.62. The
OVROUGHRTFAC = 0.4, and MANN scale factor = 1.8 are
considered to give the best results. The sensitivity of
RETDEPRTFAC and OVOUGHRTFAC is, however, not as
pronounced as that of REFKDT and MANN. The scaling factor
of MANN = 1.8 gives the calibrated manning coefficients in
the range of 0.99 to 0.02 (Table 4). The RETDEPRTFAC = 0.0
is in agreement with Yucel et al. (2015) who suggested that a
value of zero for this parameter is ideal for steep slopes like that
Table 3 Selected objective criteria (Nash-Sutcliffe efficiency (NSE)
and the RMSE observation standard deviation ratio (RSR)) between sim-
ulated and observed discharges at Tana Rukangas RGS 4BE10 based on
selected parameters: infiltration-runoff (REFKDT), retention
(RETDEPRTFAC), overland flow roughness (OVROUGHRT), and the
Mannings roughness coefficients (MANN) parameters
Range 0.6 0.8 1.0 2.0 3.0 4.0 6.0
RSR 0.860.760.710.65 0.65 0.65 0.66
NSE 0.250.410.490.58 0.57 0.57 0.56
Range 0.0 1.0 2.0 3.0 4.0 5.0
RSR 0.65 0.65 0.65 0.65 0.65 0.65
NSE 0.58 0.58 0.58 0.58 0.58 0.58
Range 0.1 0.2 0.4 0.6 0.8 1.0
RSR 0.70 0.69 0.64 0.64 0.65 0.65
NSE 0.51 0.53 0.59 0.58 0.58 0.58
Range 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
RSR 0.800.680.650.640.640.630.620.62 0.62
NSE 0.370.540.580.590.590.600.610.62 0.61
Values in italics show the criteria for the selected parameters after
1344 N. Kerandi et al.
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of MSS as there is no noticeable accumulation. Increases in the
RETDEPRTFAC on channel pixels can encourage more local
infiltration near the river channel leading to wetter soils (Gochis
et al. 2015). This will not be necessarily associated with our
case since this will reduce surface runoff further reducing the
hydrograph volumes.
The model underestimates the observed discharge. For in-
stance, at the end of the calibration process, the model is found
to simulate only 60% of the observed discharge at the Tana
Rukangas RGS 4BE10 gauge at the end of the simulation
period. But in general, the offline (uncoupled) WRF-Hydro
is able to capture reasonably the dynamics of the hydrological
regime of the MSSs streamflow. In all subsequent simula-
tions, the calibrated parameters are held as such.
3.2 Precipitation validation
3.2.1 Model results versus gridded data
Prior to the analysis of the spatially averaged precipitation
over the study area, the two modelsseasonal mean
precipitations in the inner domain are compared to those de-
rived from CHIRPS. Figures 5and 6display the spatial maps
of the MAM and OND seasonal precipitations as derived from
CHIRPS and simulated in WRF-only and coupled WRF-
Hydro. A common feature seen in the spatial maps is that
the two models exhibit a similar pattern in either of the sea-
sons. They both capture well the precipitation maximum in the
vicinity of upper Tana River basin (TRB). Thus, a clear de-
pendence of precipitation on topography is depicted. The dif-
ference between different years is evident though in general,
the two models underestimate the MAM precipitation while
they overestimate the OND precipitation especially in upper
TRB. The relationship of both WRF-only and coupled WRF-
Hydro to CHIRPS estimates is summarized in Fig. 7. Here, the
normalized statistical comparison of the monthly sums of pre-
cipitation of all MAMs and all ONDs during 2011 to 2014 is
shown. Both WRF-only and WRF-Hydro display similar spa-
tial variability with fair to good pattern correlations (r0.6)
and normalized standard deviation close to that of the
Spatially averaged precipitation over Mathioya-Sagana
subcatchment The spatially averaged precipitation from
WRF-only and coupled WRF-Hydro is compared against
two observational datasets: TRMM and CHIRPS. Figure 8
shows the four monthly time series of both the simulated
and observed precipitation. Both WRF-only and coupled
WRF-Hydro capture quite reasonably the seasonal, annual,
and interannual evolution of precipitation derived from
CHIRPS and TRMM with overall high correlation coeffi-
cients [r(46) > 0.7, p< 0.001]. The two modeling systems
capture well the seasonal peak of OND as November but
occasionally miss that of the MAM season by 1 month.
Seasonal and cumulative totals over the study area Both
models capture well the variability of the two rainy seasons,
MAM and OND, over the MSS and surrounding area. The
total seasonal simulated precipitation for the 4 years (2011 to
2014) is more than that derived from TRMM but slightly less
than that derived from CHIRPS. The respective mean annual
precipitations (i.e., for the two seasons only) are
TRMM = 795 mm/year, CHIRPS = 989 mm/year, WRF-on-
ly = 977 mm/year, and WRF-Hydro = 940 mm/year showing
a reasonable agreement but more so with CHIRPS dataset
(Table 5). During MAM, the models underestimate the ob-
served precipitation in both TRMM and CHIRPS in 2011
and 2012. The simulated amount is slightly closer to that de-
rived in CHIRPS in 2013. In OND, it is seen that both WRF-
only and WRF-Hydro overestimate the observed precipitation
in the two datasets (Fig. 9a). In terms of the cumulative pre-
cipitation, WRF-only (1392 mm/year) and coupled WRF-
Hydro (1318 mm) yielded more precipitation compared to that
derived in TRMM (1092 mm) in excess of approximately 27
Table 4 Default channel parameter values of base width (Bw), initial
water depth (HLINK), channel slope (ChSSlp), and the calibrated
Manning coefficient (MannN) based on scaling factor 1.8 corresponding
to each stream order
Stream order BW HLINK ChSSlp MannN
1 1.5 0.02 3.0 0.55
2 3.0 0.02 1.0 0.35
3 5.0 0.02 0.5 0.15
4 10 0.03 0.18 0.10
5 20 0.03 0.05 0.07
6 40 0.03 0.05 0.05
7 60 0.03 0.05 0.04
8 70 0.10 0.05 0.03
9 80 0.30 0.05 0.02
10 100 0.30 0.05 0.01
Fig. 4 Observed and simulated (uncoupled WRF-Hydro) hydrographs
and derived precipitation from TRMM at Tana Rukangas RGS 4BE10
for 2012. The year 2012 was considered for calibration
Joint atmospheric-terrestrial water balances for East Africa 1345
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and 21%, respectively (Table 5). This is not the case compared
to the total cumulative precipitation derived from CHIRPS
(1352 mm), whereby there is a closer agreement in the cumu-
lative totals for both WRF-only and WRF-Hydro with same
magnitude of excess and deficiency of 2% (Fig. 9b). It is also
seen that the coupled WRF-Hydro simulates slightly less pre-
cipitation compared to WRF-only consistent with early stud-
ies (e.g., Senatore et al. 2015).
3.2.2 Model results versus station data
The precipitations from the three stations (Nyeri, Muranga,
Sagana) over the MSS are compared to those derived from the
corresponding WRF-only and coupled WRF-Hydro grid
points. The precipitation amounts are all mean centered, i.e.,
subtracting each value from the mean of the respective series.
Figure 10 shows the resulting scatter plots. There is a fair
agreement between the shape of the monthly series: the sim-
ulated (WRF-only; coupled WRF-Hydro) and the observed
(station data). This is an indication of the two modeling sys-
tems capturing fairly well the seasonaland annual evolution of
precipitation in this region. Both the coupled WRF-Hydro and
WRF-only explain the variability of station data in a similar
manner. Further examination of the skill scores (SS) of the two
models averaging over all the stations shows that WRF-only
exhibits a lower SS of 0.01 than WRF-Hydro (SS 0.09).
Note that the SS are constructed using either mean absolute
error (MAE), mean square error (MSE), or the root-mean-
square error (RMSE). Just as the NSE, they range between
-and +1. This shows that coupled WRF-Hydro has slightly
better skill in estimating the station data than WRF-only.
Tab le 6confirms the previous results, although it is clear that
the two models underestimate the station precipitation. This is
consistent with the results discussed in Sect. 3.2.1.
3.3 River discharge
The coupled WRF-Hydro simulated river discharge is com-
pared to that observed at Tana Rukangas RGS 4BE10 for
2011 to 2014. Figure 11 shows the hydrograph of observed
and simulated discharges at a daily resolution and the corre-
sponding precipitation as simulated from coupled WRF-
Hydro over the MSS during 2011 to 2014. The simulated
and observed discharges for the entire period (2011 to 2014)
arefairlycorrelatedwithcorrelation coefficient, r
(1386) 0.52, p< 0.001, with, however, occasional lagging
Fig. 5 Precipitation maps for the
inner domainD2, averaged for the
March, April, and May (MAM)
season for the period 2011 to
2014, derived from aCHIRPS,
and the two modeling systems: b
WRF-only and ccoupled WRF-
Hydro. The red contour delin-
eates part of the TRB
1346 N. Kerandi et al.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
of simulated peaks to those observed. There is a clear corre-
spondence of the observed and simulated discharges as a
response to the rainstorms in the region. This is demonstrated
in a linear relationship between discharge and precipitation
over the catchment (correlation coefficient of 0.81). The de-
rived statistics from the simulated and observed series are
shown in Table 7. The model reasonably captures the high
flows of May, 2013, and those of OND season. The best per-
formance is obtained for the year 2013.
Fig. 6 As in Fig. 5, except for the
October, November, and
December (OND) season
Fig. 7 Taylor diagram showing normalized pattern statistics of monthly
precipitation sums for MAM and OND seasons between WRF-only/
coupled WRF-Hydro simulations and CHIRPS estimates over domain
D2, for the period 2011 to 2014
Fig. 8 Time series of monthly precipitation (in mm/day) spatially
averaged over the Mathioya-Sagana subcatchment (MSS) and surround-
ing area (see Fig. 1) derived from TRMM, CHIRPS, WRF-only, and
coupled WRF-Hydro
Joint atmospheric-terrestrial water balances for East Africa 1347
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The averaged discharge observed at RGS 4BE10 during
this period was 36.0 m
/s (or 346 mm/year), while the corre-
sponding simulated discharge is approximately 34.7 m
/s (or
334 mm/year), i.e. only approximately 3% difference. The
corresponding values for precipitation in the delineated MSS
are simulated = 988 mm/year, observed precipitation in
CHIRPS = 1263 mm/year, and in TRMM = 1059 mm/year.
This gives a discharge-precipitation coefficient between 0.26
and 0.33 for the observational datasets considered and 0.34 for
the simulation.
The low and even negative NSE of simulated discharge for
some years has also been achieved in neighboring basins (e.g.,
Mango et al. 2011;Obiero2011). There are also differences in
the performance between the uncoupled WRF-Hydro model
in calibration and the coupled WRF-Hydro, perhaps partially
due to the different frequency that the Noah LSM is called in
the offline calibration and during the coupled runs (Senatore
et al. 2015). Besides, this may be attributed to the different
forcing data that drives the coupled and uncoupled WRF-
Hydro modeling systems. Though, in general, the coupled
WRF-Hydro just like in its uncoupled mode captures reason-
ably the hydrological dynamics of the basin.
3.4 Water balance results
3.4.1 Terrestrial water balance
This section is based on the theory given in Sect. 2.4.1.The
seasonal and interannual variation of precipitation (P),
Fig. 9 a As in Fig. 8, except for seasonal (MAM and OND) and b
cumulative precipitation
Table 5 Selected statistics (mean
absolute error (MAE), root mean
square error (RMSE), correlation
coefficient (r), and the percent
bias (Pbias)) between simulated
WRF-only and coupled WRF-
Hydro and derived precipitation
from TRMM and CHIRPS spa-
tially averaged over the
Mathioya-Sagana subacatchment
(MSS) and surrounding area for
the period 2011 to 2014
Experiment MAE (mm/day) RMSE (mm/day) rPbias (%)
TRMM WRF-only 1.4 1.9 0.8 23.1
WRF-Hydro 1.5 2 0.77 20.5
CHIRPS WRF-only 1.5 2.1 0.75 0.6
WRF-Hydro 1.6 2.2 0.73 2.7
Total precipitation (mm/year)
TRMM 1092
WRF-only 1392
WRF-Hydro 1318
Fig. 10 Scatter plot between mean-centered monthly precipitation time
series from the simulations (WRF-only, coupled WRF-Hydro) and station
measurements at Nyeri, Sagana, and Muranga for 2011 to 2014. The
correlation coefficients refer to each station in the order: Nyeri, Sagana,
and Muranga, respectively
1348 N. Kerandi et al.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
evapotranspiration (ET), discharge (R), and change in terres-
trial water storage (dS/dt) over the MSS is presented here. The
terrestrial water balance (TWB) components in this study are
processed from the WRF-only and coupled WRF-Hydro
modeling system. P, ET, and Rare directly derived from the
model outputs, while dS/dt is calculated as a residue, i.e., dS/
dt =P(ET + R). Table 8and Figure 12 show the seasonal
and interannual variability of these TWB components simu-
lated by WRF-only and coupled WRF-Hydro and are
discussed in the following subsections.
Precipitation The monthly evolution and interannual vari-
ability of Pfor both WRF-only and coupled WRF-Hydro
exhibit striking similarity (see Sect. 3.2). For the period
2011 to 2014, WRF-only yields an average of 3.7 mm/day,
while coupled WRF-Hydro yields slightly less, i.e., 3.6 mm/
Discharge Simulated discharge in the coupled WRF-Hydro
and total runoff from WRF-only show similar seasonality and
interannual variability. Both WRF-only and coupled WRF-
Hydro indicate April as the peak month for the AMJ season,
while they indicate November as the peak month for the OND
season. The April peak comes slightly earlier than the clima-
tological peak of May. However, this shows a very close re-
lationship with P. The 4-year (2011 to 2014) average is
0.90 mm/day for WRF-only, while in the case of coupled
Table 6 As in Table 5but between nearest grid point of WRF/WRF-Hydro and observed stations(Nyeri, Sagana, Muranga) precipitation for the
period 2011 to 2014
Experiment MAE (mm/day) RMSE (mm/day) rPbias (%)
Nyeri WRF-only 1.4 2.3 0.52 27.3
WRF-Hydro 1.5 2.4 0.46 35.1
Sagana WRF-only 2.2 4.2 0.53 41.8
WRF-Hydro 2.2 4.2 0.56 48.0
Muranga WRF-only 2.1 3.4 0.56 41.2
WRF-Hydro 2.1 3.4 0.61 47.4
Total precipitation (mm/year)
Nyeri Observed 917
WRF-only 436
WRF-Hydro 407
Sagana Observed 1140
WRF-only 605
WRF-Hydro 501
Muranga Observed 1127
WRF-only 632
WRF-Hydro 551
WRF-only Common grid point 668
WRF-Hydro Common grid point 594
The total precipitation for the whole period is also provided
Fig. 11 Observed and simulated (coupled WRF-Hydro) hydrographs
and precipitation in the Mathioya-Sagana subcatchment (MSS) for the
period 2011 to 2014 at Tana Rukangas RGS 4BE10
Table 7 Selected objective criteria as in Table 3but between simulated
coupled WRF-Hydro discharge and that recorded at the 4BE10 gauge for
the period 2011 to 2014
20112014 2011 2012 2013 2014
Daily time step
NSE 0.02 0.17 0.21 0.49 1.02
RSR 0.99 1.08 1.10 0.71 1.42
Monthly time step
NSE 0.15 0.35 0.85 0.71 1.43
RSR 0.91 0.77 1.30 0.51 1.49
Joint atmospheric-terrestrial water balances for East Africa 1349
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WRF-Hydro, it is slightly higher, i.e., 0.93 mm/day. This is
also close to that observed which is, on average, 0.95 mm/day.
In terms of monthly evolution, WRF-only series was found to
correlate to that of observed fairly (r0.68, p<001)com-
pared to WRF-Hydro (r0.62, p< 0.001). The performance
in 2011 shows that the coupled WRF-Hydro yields less runoff
than WRF-only as expected owing to lateral soil water redis-
tribution that leads to higher water storage in the soil. This is
not the case for the other years, i.e., 2012, 2013, and 2014,
which determines the mean performance of coupled WRF-
Hydro and WRF-only to be different from expectations.
This can be attributed to a large contribution of exfiltration
due to the high elevation in MSS. In this regard, the coupled
WRF-Hydro decreases surface runoff by allowing surface wa-
ter to infiltrate at different time steps at different locations on
one hand and on the other hand allowing exfiltration, which
increases surface runoff.
Evapotranspiration The monthly and interannual variation
of ET as simulated by WRF-only and coupled WRF-Hydro is
equally similar. The 4-year average for WRF-only is 2.2 mm/
day and that of coupled WRF-Hydro is 2.1 mm/day, i.e.,
slightly less. ET displays small monthly variation throughout
the period of 2011 to 2014. Lowest values occur during the
months of March and August, while the peak months with
highest values fall during the months of April and
DecemberJanuary. During the peak season of ET, the plants
and environment transfer large quantities of water vapor into
the atmosphere recovered from the immediate rainy season.
Change in terrestrial water storage The monthly evolution
and interannual evolution of dS/dt exhibit seasonality with
peak values in the months of April and November. The 4-
year average value of dS/dt derived from WRF-only is
0.72 mm/day compared to that from coupled WRF-Hydro of
0.68 mm/day. This is consistent with a reduction of precipita-
tion and increase of runoff in coupled WRF-Hydro, in com-
parison to WRF-only. On the monthly scale, dS/dt displays
both negative values and positive values. The negative or
low values are dominant during the months of January
February and June. Both models exhibit similar interannual
Relationship between Weather Research and Forecasting-
only and coupled Weather Research and Forecasting-
Hydro terrestrial water balance components The monthly
differences between the TWB components as simulated by the
two models are shown in Fig. 13. The magnitudes of the
average differences for all TWB components are very small
(between 0.03 and 0.08 mm/day). Precipitation shows the
highest magnitude, while discharge shows the least. It is only
in the simulated discharge that coupled WRF-Hydro yields
more than the WRF-only model.
Table 8 Annual averages of the
atmospheric and terrestrial water
balance components (AWB and
TWB) (mm/day) from Eqs. 1and
(4), as simulated in WRF-only
and coupled WRF-Hydro for
Mathioya-Sagana subcatchment
and surrounding areas during
2011 to 2014
Experiment 2011 2012 2013 2014 4-year mean
dW/dt WRF-only AWB 0.00 0.01 0.00 0.01 0.00
WRF-Hydro TWB 00.0 0.02 0.00 0.01 0.00
dS/dt WRF-only TWB 1.00 0.94 0.46 0.48 0.72
WRF-Hydro TWB 1.07 0.90 0.26 0.47 0.68
IN WRF-only AWB 118.50 118.80 119.98 122.18 119.86
WRF-Hydro AWB 117.99 117.28 117.73 120.87 118.87
OUT WRF-only AWB 117.14 117.28 118.87 121.60 118.72
WRF-Hydro AWB 116.72 117.30 116.75 120.27 117.76
CWRF-only AWB 1.36 1.52 1.11 0.58 1.14
WRF-Hydro AWB 1.27 1.59 0.98 0.60 1.11
PWRF-only AWB 3.14 3.71 3.41 2.51 3.19
TWB 3.67 4.19 3.91 2.94 3.68
WRF-Hydro AWB 3.06 3.75 3.27 2.32 3.10
TWB 3.62 4.22 3.77 2.80 3.60
ET WRF-only AWB 1.79 2.21 2.31 1.92 2.06
TWB 1.78 2.17 2.30 1.93 2.06
WRF-Hydro AWB 1.79 2.17 2.30 1.72 1.99
TWB 1.78 2.17 2.30 1.73 2.00
RWRF-only TWB 0.87 1.05 1.14 0.53 0.90
WRF-Hydro TWB 0.77 1.14 1.22 0.60 0.93
1350 N. Kerandi et al.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The evolution of monthly differences in dS/dt and Pin both
models shows similar patterns with higher differences in the
peak months of the MAM and OND seasons. On average, the
differences for all TWB components are minimal and constant
during the dry months of June to October. This shows that in
the absence of Pand ET differences between WRF-only and
WRF-Hydro, differences in the other components are equally
insignificant. In the case of dS/dt and R, the sign of the
differences often alternates; i.e., increased (reduced) runoff
leads to lowering (increasing) of the amount of soil moisture.
This is common during the peak months of the rainy seasons
of MAM and OND.
The annual difference PET for the two models yields
same values (Table 8). On average, these differences are
1.6 mm/day. On the other hand, the annual difference PR
for WRF-only is 2.8 mm/day and that for coupled WRF-
Hydro is 2.7 mm/day. This shows that there is more abun-
dance of soil recharge in this area according to WRF-only than
coupled WRF-Hydro. However, in the months of January to
March, there is a soil deficit as ET > P(Fig. 13). This is an
indication that evapotranspiration is more critical during these
months than during the rainy seasons.
3.4.2 Atmospheric water balance
The basic theory of the atmospheric water balance (AWB) is
presented in Sect. 2.4.2. All the variables are averaged over a
rectangular boundary encompassing the Mathioya-Sagana
subcatchment (MSS). The simulated variables from the
WRF-only and coupled WRF-Hydro models for the 4-year
Fig. 12 Monthly time series of the terrestrial water balance (TWB) com-
ponents (Eq. 1) in millimeter per day, as simulated in aWRF-only and b
coupled WRF-Hydro, averaged over the Mathioya-Sagana subcatchment
(MSS) and surrounding area (see Fig.1) for the period 2011 to 2014
Fig. 13 As in Fig. 12, except for the difference between WRF-only and
WRF-Hydro (coupled WRF-Hydro minus WRF-only)
Fig. 14 As in Fig. 12, except for the monthly atmospheric water balance
(AWB) components (Eq. (4))
Joint atmospheric-terrestrial water balances for East Africa 1351
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(2011 to 2014) climatology are displayed in Table 8and
Figs. 14 and 15 and discussed in this section.
Precipitation and evapotranspiration The monthly and in-
terannual variation of Pand ET has been discussed in Sect.
3.4.1 for the TWB. Pis considered a loss from the atmosphere
and a gain for the terrestrial surface, while ET is obviously a
gain for the atmosphere and a loss from the surface. ET
reaches its peak in May for the MAM season, 1 month after
that of P.
Atmospheric moisture convergence The atmospheric mois-
ture convergence Cmonthly and interannual variation follows
that of precipitation. Creaches its peak in April and
November, which are the peak months of the two rainy sea-
sons (i.e., MAM and OND) in this region. The lowest values
are during the months of January.
Atmospheric water storage The atmospheric water storage,
dW/dt, hardly shows any monthly or interannual variation in
both WRF-only and coupled WRF-Hydro. It is very small
compared to the other terms and tends to zero, as expected
for a regional water balance.
Relationship between Weather Research and Forecasting-
only and coupled Weather Research and Forecasting-
Hydro components The monthly differences between
coupled WRF-Hydro and WRF-only AWB components are
summarizedand displayed in Fig. 15. The differences in Pand
C display a similar pattern over the years. This implies that the
differences in Poriginate from differences in C. This is asso-
ciated with the impact of moisture vapor influx into the do-
main whose average magnitude for the 4-year period is greater
than that of vapor outflow in both models. However, in indi-
vidual years, the models display larger differences, especially
during the years when the coupled WRF-Hydro yields more C
than WRF-only model. The differences in ET and dW/dt are
comparatively smaller with, however, the year 2014 having
the highest difference for the case of ET (0.20 mm/day).
3.5 Land-atmospheric interactions
within Mathioya-Sagana subcatchment
This section is based on Eqs. 5and 6mentioned in Sect. 2.4.2
on the atmospheric bulky properties, i.e., the recycling ratio β
and the precipitation efficiency χ. The two measures are used
to analyze the land-atmospheric interactions and feedback be-
tween the land and atmosphere in the study area.
Recycling ratio βFigure 16a shows the mean annual cycle of
the recycling ratio βfor the period 2011 to 2014 as simulated
in the WRF-only and coupled WRF-Hydro. In general, the
value of βis high whenever there are low moisture influx
and high evapotranspiration (e.g., Asharaf et al. 2012). βis
seen to vary from 0.01 to 0.04. High values of βoccur during
the months of January that exhibit largest amount of ET (dom-
inant compared to P). In terms of the rainy seasons, i.e., MAM
and OND, it is noticed that βremains below 0.02. This implies
that precipitation originating from evapotranspiration in the
MSS region, i.e., the study area, contributes little to total pre-
cipitation in this region during the quadrennial. It is concluded
that local precipitation in the MSS region does not depend
significantly on the state of the land surface and that potential
land-precipitation feedback mechanisms have a reduced im-
pact in this region.
Precipitation efficiency χThe mean annual cycle of the pre-
cipitation efficiency χis displayed in Fig. 16b. Two distinct
Fig. 15 As in Fig. 14, except for the difference between WRF-only and
WRF-Hydro (coupled WRF-Hydro minus WRF-only)
Fig. 16 Mean annual cycle of a
recycling ratio and bprecipitation
efficiency as simulated in WRF-
only and coupled WRF-Hydro
over the MSS and surrounding
area (Fig. 1) for the years 2011 to
1352 N. Kerandi et al.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
seasons similar to those of the rainy seasons, i.e., MAM and
OND, are depicted. The values of χfor the two models are in
the range of 0.0 and 0.07 and reach their peaks during the
months of April and November. Low χfurther implies that
only a small portion of the atmospheric water inflow does
contribute to most of the precipitation in the MSS region.
4 Summary and conclusion
The hydrometeorology of the Mathioya-Sagana subcatchment
(MSS) and its surrounding in the upper Tana River basin
(TRB), Kenya, East Africa, has been investigated in terms of
its terrestrial and AWB components. This has been achieved
through the application of the coupled WRF-Hydro modeling
system whose results have been compared to the WRF-only
As a first step towards coupled WRF-Hydro simulations,
the uncoupled WRF-Hydro calibration was carried out. The
model reproduced the observed discharge at the Tana
Rukanga RGS 4BE10, which is located at the mouth of
MSS during the year 2012. The uncoupled WRF-Hydro reg-
istered good results of NSE = 0.62, with, however, an under-
estimation of 40% of the observed discharge.
Prior to the analysis of the water balance components, the
WRF-only and coupled WRF-Hydro modelsperformance in
simulated precipitation were compared to station data sourced
from the stations located in MSS and also to TRMM and
CHIRPS datasets. Both models showed reasonable correspon-
dence with respect to the station data and the two gridded
datasets. The models captured well the seasonal, annual, and
interannual evolution of both datasets. The models exhibit
similar performance most of the time especially on the 4-
year mean averages.
Results in simulated discharge for the coupled WRF-Hydro
for the period 2011 to 2014 showed consistency with that
simulated in uncoupled WRF-Hydro. The averaged simulated
discharge (34.7 m
/s) in the coupled case matched closely that
recorded at RGS 4BE10 gauge (36 m
The simulated water balance components in the WRF-only
and coupled WRF-Hydro exhibited similar seasonal, annual,
and interannual variability for the period 2011 to 2014. Most
of the components had their peak during April/May and
November. This was influenced by the moisture transport into
the study area. The smallest variation was for the case of evapo-
transpiration (ET) and atmospheric water storage (dW/dt).
During the rainy seasons of MAM and OND, the soils were
replenished with enough moisture as P>ETaswellasthe
associated water vapor convergence. ET in this region was seen
to be playing a greater role during the dry months (January
March) with associated atmospheric divergence. Among the
water balance tendencies, the terrestrial water storage, dS/dt,
did not tend towards zero even after 1 year or the entire period
of study. However, dW/dt tendedtozeroinperiodinmonth
duration and became negligible in annual or longer.
The intensity of the water cycle has been quantified in
terms of recycling ratio and precipitation efficiency. On the
monthly scale, the magnitude of the recycling ratio was small,
ranging from 0.0 to 0.04, while that of precipitation efficiency
ranged between 0.0 and 0.07. This indicates that precipitation
in this region during this period mainly originates from water
vapor inflow at the lateral boundaries of the domain, so that
potential land-precipitation feedback mechanisms have a re-
duced impact in this region at this scale.
Based on our research study, compared to WRF-only, the
coupled WRF-Hydro slightly reduces precipitation, evapo-
transpiration, and the soil water storage but increases runoff.
Thus, the impact of coupled WRF-Hydro in simulation of
runoff shows deviation from expectation, probably because
of the strong orographic forcing in our region. Further, the
magnitude and differences between land-atmospheric feed-
back mechanisms, which include the precipitation efficiency
and recycling ratio, are small.
The coupled WRF-Hydro serves as a tool in quantifying
the atmospheric-TWB for this region. Further studies with a
larger area covering the whole of TRB and East Africa would
allow testing the impact of local recycling at larger scales and
improve our understanding of land-atmosphere feedback
mechanisms. In the long run, such studies may lead to sug-
gestions of better management practices of the scarce water
resources over the region.
Acknowledgements We acknowledge the financial support for this
work from the German Academic Exchange Service (DAAD) and the
National Commission for Science, Technology and Innovation
(NACOSTI) on behalf of the government of Kenya. Dr. Arnault was
funded by DFG within the subproject BA5The role of soil moisture
and surface- and subsurface water flows on predictability of convection^
of the Transregional Collaborative Research Center SFB/TRR 165
BWav es To We at her.^Further, we acknowledge support by the
WASCAL project funded by the German Ministry of Education and
Research (BMBF).
We appreciate the Leibniz Supercomputing Centre (LRZ) and the
Karlsruhe Institute of Technology and its Institute of Meteorology and
Climate Research Atmospheric Environmental Research (KIT/IMK-IFU),
Campus Alpin, for providing library and computing facilities. We also like
to thank the European Centre for Medium-Range Weather Forecasts
(ECMWF) for providing the ERA-Interim reanalysis data and products.
We recognize the Kenya Meteorological Services (KMS) for provid-
ing the monthly rainfall data and the Kenya Water Resource Management
Authority (WRMA) for providing discharge data, rainfall data, and the
shape files used in this study. We thank the two anonymous reviewers for
their valuable comments.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://, which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appro-
priate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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... In the case of WRF-Hydro, a complex land surface model, with an explicit description of river streamflow, is employed. The uncoupled WRF-Hydro model consists of a variety of parameters (e.g., ref. [48]), which usually require calibration. Since the aim of the research is to evaluate the performance of WRF-H to simulate discharge, and therefore analyze its predicting skills about floods, the calibration is performed based on discharge at the Savè catchment outlet (Figure 1). ...
... The default simulation (REFKDT = 3.0) realized had shown an underestimation of the observed streamflow because the REFKDT controls the infiltration of the surface water, and its value shall be reduced to disable many infiltrations. As illustrated with [49] for the case of the Sissili in West Africa, and [48] in Kenya (East Africa), we found that the model discharge performance is highly sensitive to parameter REFKDT. For the case in study, the REFKDT = 1.5 performs better than using the default value (3.0) with statistics (NSE = 0.52, KGE = 0.49, and Corr = 0.58). ...
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Since the beginning of the 2000s, most of the West-African countries, particularly Benin, have experienced an increased frequency of extreme flood events. In this study, we focus on the case of the Ouémé river basin in Benin. To investigate flood events in this basin for early warning, the coupled atmosphere–hydrology model system WRF-Hydro is used, and analyzed for the period 2008–2010. Such a coupled model allows exploration of the contribution of atmospheric components into the flood event, and its ability to simulate and predict accurate streamflow. The potential of WRF-Hydro to correctly simulate streamflow in the Ouémé river basin is assessed by forcing the model with operational analysis datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF). Atmospheric and land surface processes are resolved at a spatial resolution of 5 km. The additional surface and subsurface water flow routing are computed at a resolution of 500 m. Key parameters of the hydrological module of WRF-Hydro are calibrated offline and tested online with the coupled WRF-Hydro. The uncertainty of atmospheric modeling on coupled results is assessed with the stochastic kinetic energy backscatter scheme (SKEBS). WRF-Hydro is able to simulate the discharge in the Ouémé river in offline and fully coupled modes with a Kling–Gupta efficiency (KGE) around 0.70 and 0.76, respectively. In the fully coupled mode, the model captures the flood event that occurred in 2010. A stochastic perturbation ensemble of ten members for three rain seasons shows that the coupled model performance in terms of KGE ranges from 0.14 to 0.79. Additionally, an assessment of the soil moisture has been developed. This ability to realistically reproduce observed discharge in the Ouémé river basin demonstrates the potential of the coupled WRF-Hydro modeling system for future flood forecasting applications.
... The WRF-Hydro is a distributed, multi-physics hydrometeorological model system created by the United States National Center for Atmospheric Research (NCAR) to address major water challenges, including operational flash flood monitoring. Many studies have utilized this modeling system to examine the model's performance and applicability, including flood predictions, water balance, and water management studies across the globe (Kerandi et al., 2018;Li et al., 2017). Despite the studies compare the impact of different sources of precipitation input (i.e., comparing observed and simulated) on runoff simulation and agree that further improvement in the precipitation simulation skills is still needed (Givati et al., 2016;Senatoreet al., 2015), not many studies have investigated the impact of the spatio-temporal resolution of various SST sources over the runoff predictions of WRF-Hydro modelling system via the improvements in the simulated precipitation. ...
... The evaluation of simulated hourly streamflow at four different locations in the study region revealed that the simulation with groundwater representation was far more capable in capturing the dynamics, Fersch, Senatore, et al., 2020;Kerandi et al., 2017;Lahmers et al., 2019;Yucel et al., 2015), Compared to the baseline WRF-Hydro configuration, the simulation with MMF groundwater scheme increases the computational time by less than 5%, which would not add significantly to computational cost in long-term simulations. This is achieved by allowing for a series of simplifying assumptions in terms of model physics, as well as the structure and properties of the subsurface. ...
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The interactions between the atmosphere and the land‐surface are characterized by complex, non‐linear processes on varying time scales. The Noah‐MP is a medium complexity land‐surface model (LSM), which was recently selected as the new default LSM for the hydrologically enhanced Weather Research and Forecasting modeling system (WRF‐Hydro). Compared to its predecessor, several parameterizations were considerably improved and new ones added, inter alia more sophisticated groundwater descriptions, which aim to replace the traditional free‐drainage lower boundary condition. This study investigates the benefits that can be obtained from a two‐dimensional groundwater representation within the WRF‐Hydro modeling system by performing two offline simulations for the upper Danube river basin. In comparison to the free‐drainage reference simulation, the lateral routing of groundwater and the two‐way interaction with the water table greatly enhances small scale variability in simulated fields of soil moisture content and evapotranspiration (ET). The representation of upward fluxes from the aquifer helps to maintain higher soil moisture contents and thus ET during prolonged dry periods. These differences are rather small though (< 2%) and explained by the fact that the study region is considered to be limited by radiative energy and not water availability. The most striking difference however is the performance gap in simulating streamflow. WRF‐Hydro with 2d groundwater scheme clearly outperforms the reference simulation in terms of performance metrics. A comparison with hourly streamflow observations for the water year of 2016 yields average Kling‐Gupta efficiencies of 0.79 vs. 0.57 for the reference. Given that both model configurations were not calibrated beforehand, we conclude that the two‐dimensional groundwater option is especially beneficial for applications in poorly or even ungauged catchments. Furthermore, the inclusion of a so far missing compartment of the water cycle in the WRF‐Hydro modeling system allows for a more holistic representation of interactions between atmosphere land surface and subsurface, which will be advantageous in feedback studies with the fully coupled WRF‐Hydro. This article is protected by copyright. All rights reserved.
... Moreover, the meteorological department of Madagascar has approved CRU data for use over the country (La Diréction Générale de . Various other studies have also employed the data Kerandi et al. 2018;Ngoma et al. 2021). The SST data from the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5) was used to determine the connection between Madagascar's rainfall and IO SST. ...
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Understanding rainfall variability over a region is essential for economic development since it contributes to managing climate-sensitive sectors. This study assesses the interannual characteristics of rainfall over Madagascar and the possible influence of the Indian Ocean (IO) on rainfall patterns between 1950 and 2018, using monthly rainfall data from the Climatic Research Unit. The singular value decomposition analysis was used to examine the effect of IO’s sea surface temperature (SST) on the rainfall variability, followed by a composite analysis of vertically integrated moisture flux. Overall, both the wet and dry seasons showed a slight decrease in rainfall trends during the past 69 years. During the wet (dry) season, the central and southeastern (western) IO SST were positively correlated with Madagascar’s rainfall. The covariability analysis showed that a positive phase of the Subtropical Indian Ocean Dipole or Indian Ocean Dipole results in increased rainfall over the northwestern (northern) and southern parts of Madagascar during the wet (dry) season. The composite analysis suggests that enhanced (decreased) rainfall during the wet (dry) years of the wet and dry seasons in Madagascar is linked to a strong moisture convergence (divergence) accompanied by strong easterlies (anticyclonic circulation) over the northwestern (southern) IO. The study findings could drive further studies on other likely factors that impact the country’s rainfall.
The coupled atmospheric-hydrologic systems help achieve deeper understanding on the interactions between the atmospheric and land-surface processes, improve the spatial and temporal accuracy of hydrologic forecasts and extend the forecast leading time. WRF-Hydro is nowadays a widely used hydrologic module to be coupled with the mesoscale numerical weather model WRF for atmospheric-hydrologic research and applications. The structure of WRF-Hydro is improved and expanded in this study to better adapt to the complicated rainfall-runoff transformation mechanism in the mixed runoff generation regions. The infiltration parameterisation is replaced by an infiltration equation that takes into account the impact of the surface soil moisture variation on the infiltration capacity, and the spatial discretisation of the infiltration capacity from a distribution curve is achieved based on the conception of the topographic index. For the runoff convergence, the river channel leakage loss is introduced into the Muskingum-Cunge (MC) streamflow calculations with variable parameters in time. Efforts are also made to reduce parameter calibration errors in WRF-Hydro. The accuracy of the WRF rainfall forcing is improved by merging weather radar and rain gauge observations using the Kriging with external drift (KED) interpolation tool. The key parameters of WRF-Hydro are then calibrated using the improved rainfall forcing with the merging observations. The performance of the improved WRF-Hydro structure is explored in both one-way and the fully coupled modes with the WRF model. Typical rainstorm events are selected from semi-humid and semi-arid catchments of Northern China as case studies. Results have shown that the improved WRF-Hydro is more effective in simulating floods with high peaks and steep rises-and-falls, but a problem is also shown of receding more quickly for low peaks. The improved model structure has changed the proportion of the mixed runoff generation. In the long term, there is an increase in the infiltration-excess runoff generation and decrease in the saturation-excess amount. Both the one-way and the fully coupled WRF/WRF-Hydro systems have demonstrated the suitability of the improved structure for rainfall-runoff processes dominated by the infiltration-excess runoff generation.
Joint hydrologic‐atmospheric model frameworks offer novel insights into the terrestrial hydrologic cycle and the potential for improved predictive capabilities for stream discharge and other hydrologic fluxes. In this study, we examine both one‐ and two‐way coupled integrations of the Weather Research and Forecasting (WRF v3.8.1) atmospheric and WRF‐Hydro (v5.0) hydrologic models for four 1000–2000 km2 snow‐dominated mountain watersheds (1500–2100 m mean elevation) in Idaho's Rocky Mountains. In watersheds where anthropogenic withdrawals are minimal (3 of 4 watersheds), we simulate stream discharge with high confidence (KGE >0.63) for a 20 year period in the uncoupled scenarios, and find that WRF winter precipitation accumulations have less have less than 15% average error for all but two of the fourteen comparison NRCS Snotel sites. However, annual streamflow biases are highly correlated (r2 > 0.8 in some cases) with the annual errors in WRF cold‐season precipitation, suggesting that process representation of winter orographic precipitation limits hydrologic predictability. In the second part of the study, we evaluate the potential for “two‐way” model coupling to influence hydrologic predictability by examining a two month case‐study period with active spring season convective precipitation. We quantify the impacts of resolving hillslope‐scale soil water redistribution on the ABL, and find that while resolving overland and saturated subsurface soil moisture flow influences soil moisture distributions and surface energy fluxes, the impact on precipitation is non‐systematic, as precipitation is generally atmospherically controlled during the study period. Consequently, future efforts should focus on improving winter orographic process representation, as streamflow is highly sensitive to errors in these processes. This article is protected by copyright. All rights reserved.
Moisture recycling plays a crucial role in regional hydrological budgets. The isotopic composition of precipitation has long been considered as a good tracer to investigate moisture recycling. This study quantifies the moisture recycling fractions (fr) in the Lake Taihu region using spatial variations of deuterium excess in precipitation (dP) and surface water vapour flux (dE). Results show that dP at a site downwind of the lake was higher than that at an upwind site, indicating the influence of lake moisture recycling. Spatial variations in dP after sub-cloud evaporation corrections were 2.3, 1.4 and 3.2 ‰, and dE values were 27.4, 32.3 and 31.4 ‰ for the first winter monsoon, the summer monsoon and the second winter monsoon, respectively. Moisture recycling fractions were 0.48 ± 0.13, 0.07 ± 0.03 and 0.38 ± 0.05 for the three monsoon periods, respectively. Both using the lake parameterization kinetic fractionation factors or neglecting sub-cloud evaporation would decrease fr, and the former has a larger influence on the fr calculation. The larger fr in the winter monsoon periods was mainly caused by lower specific humidity of airmasses but comparable moisture uptake along their trajectories compared to the summer monsoon period.
Drought is a complex and slow-moving disaster that is difficult to monitor and define. This study, therefore, aims to demonstrate the characteristics of recent droughts occurring from 2008 to 2015 over South Korea using a process-based land and hydrologic model, Weather Research and Forecasting-Hydro modeling system (WRF-Hydro). To drive the standalone WRF-Hydro, gridded meteorological data (5 km) were generated using station-based observations and the Parameter-elevation Regressions on Independent Slopes Model (PRISM). The model was calibrated and evaluated using inflow observations at four locations with dams; for 2008–2010 (calibration) and 2011–2015 (evaluation), it demonstrated average R² values of 0.80 and 0.75, respectively. While Standardized Precipitation Index is used for calculating meteorological drought using precipitation from PRISM, Standardized Soil Moisture Index and Standardized Streamflow Index, at different timescales, are used to calculate agricultural and hydrological droughts, respectively, with WRF-Hydro simulations. The correlation coefficients between SPI and both SSFI and SSMI were calculated to detect their response times. The hydrological and agricultural droughts showed response times 0.5–1 month later than meteorological drought. In 2008–2015, agricultural and hydrological drought events occurred 1.2 times per year on average in South Korea, whereas meteorological droughts occurred 3.2 times per year on average. Agricultural and hydrological droughts lagged behind meteorological droughts by up to 53, 65, and 83 days, when using 1-, 3-, and 6-month SPI, respectively. Moreover, hydrological droughts were less severe than meteorological droughts due to the propagation of drought by attenuation. This study demonstrates that WRF-Hydro can be used to quantitatively determine the different types of drought events and their propagation, which could help policy makers manage drought risks.
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Urban conurbations of East Africa are affected by harmful levels of air pollution. The paucity of local air quality networks and the absence of capacity to forecast air quality make it difficult to quantify the real level of air pollution in this area. The chemistry-transport model CHIMERE has been coupled with the meteorological model WRF and used to simulate hourly concentrations of Particulate Matter PM2.5 for three East African urban conurbations: Addis Ababa in Ethiopia, Nairobi in Kenya and Kampala in Uganda. Two existing emission inventories were combined to test the performance of CHIMERE as an air quality tool for a target monthly period of 2017 and the results compared against observed data from urban and rural sites. The results show that the model is able to reproduce hourly and daily temporal variability of aerosol concentrations close to observations both in urban and rural environments. CHIMERE’s performance as a tool for managing air quality was also assessed. The analysis demonstrated that despite the absence of high-resolution data and up-to-date biogenic and anthropogenic emissions, the model was able to reproduce 66–99 % of the daily PM2.5 exceedances above the WHO 24-hour mean PM2.5 guideline (25 µg m−3) in the three cities. An analysis of the 24-hour mean levels of PM2.5 was also carried out for 17 constituencies in the vicinity of Nairobi. This showed that 47 % of the constituencies in the area exhibited a low air quality index for PM2.5 in the unhealthy category for human health exposing between 10000 to 30000 people/km2 to harmful levels of air contamination.
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Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high-resolution (0.05°) global precipitation climatologies that perform reasonably well in data-sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate Hazards Group's Precipitation Climatology version 1 (CHPclim v.1.0, doi:10.15780/G2159X), is shown to compare favorably with similar global climatology products, especially in areas with complex terrain and low station densities.
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This study investigates the ability of the regional climate model Weather Research and Forecasting (WRF) in simulating the seasonal and interannual variability of hydrometeorological variables in the Tana River basin (TRB) in Kenya, East Africa. The impact of two different land use classifications, i.e., the Moderate Resolution Imaging Spectroradiometer (MODIS) and the US Geological Survey (USGS) at two horizontal resolutions (50 and 25 km) is investigated. Simulated precipitation and temperature for the period 2011–2014 are compared with Tropical Rainfall Measuring Mission (TRMM), Climate Research Unit (CRU), and station data. The ability of Tropical Rainfall Measuring Mission (TRMM) and Climate Research Unit (CRU) data in reproducing in situ observation in the TRB is analyzed. All considered WRF simulations capture well the annual as well as the interannual and spatial distribution of precipitation in the TRB according to station data and the TRMM estimates. Our results demonstrate that the increase of horizontal resolution from 50 to 25 km, together with the use of the MODIS land use classification, significantly improves the precipitation results. In the case of temperature, spatial patterns and seasonal cycle are well reproduced, although there is a systematic cold bias with respect to both station and CRU data. Our results contribute to the identification of suitable and regionally adapted regional climate models (RCMs) for East Africa.
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A closed description of the regional water balance requires hydro-meteorological modeling systems which represent the atmosphere, land surface and subsurface. We developed such a meso-scale modeling system, extending the atmospheric model WRF with the distributed hydrological model HMS in a fully coupled way. It includes explicit lateral groundwater and land surface flow parameterization schemes and two-way groundwater-unsaturated zone interaction by replacing the free drainage bottom boundary of WRF's Noah-LSM with a Fixed-head or Darcy-flux boundary condition. The system is exemplarily applied for the Poyang Lake basin (160,000 km2) and the period 1979-1986 using a two-nest approach covering East Asia (30 km) and the Poyang Lake basin (10 km) driven by ERA Interim. Stand-alone WRF effectively simulates temperature (bias 0.5°C) and precipitation (bias 21-26%). Stand-alone HMS simulations provide reasonable streamflow estimates. A significant impact on the regional water balance was found if groundwater-unsaturated zone interaction is considered. But the differences between the two groundwater coupling approaches are minor. For the fully-coupled model system, streamflow results strongly depend on the simulation quality for precipitation. Two-way interaction results in net upward water fluxes in up to 25% of the basin area after the rainy season. In total, two-way interaction increases basin averaged recharge amounts. The evaluation with CPC and GLEAM indicates a better performance of the fully coupled simulation. The impact of groundwater coupling on LSM and atmospheric variables differs. Largest differences occur for the variable recharge (26%), whereas for atmospheric variables the basin-averaged impact is minor (<1%). But locally, a spatial redistribution up to ± 5% occurs for precipitation. This article is protected by copyright. All rights reserved.
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With the aim of developing a fully coupled atmosphere-hydrology model system, the Weather Research and Forecasting (WRF) Model was enhanced by integrating a new set of hydrologic physics parameterizations accounting for lateral water flow occurring at the land surface. The WRF-Hydro modeling system was applied for a three-year long simulation in the Crati River Basin (Southern Italy), where output from the fully-coupled WRF/WRF-Hydro was compared to that provided by original WRF model. Prior to performing coupled land-atmosphere simulations the stand-alone hydrological model (‘uncoupled' WRF-Hydro) was calibrated through an automated procedure and validated using observed meteorological forcing and streamflow data, achieving a Nash-Sutcliffe Efficiency value of 0.80 for one year of simulation. Precipitation, runoff, soil moisture, deep drainage and land surface heat fluxes were compared between WRF-only and WRF/WRF-Hydro simulations and validated additionally with ground based observations, a FLUXNET site and MODIS derived LST. Since the main rain events in the study area are mostly dependent on the interactions between the atmosphere and the surrounding Mediterranean sea, changes in precipitation between modeling experiments were modest. However, redistribution and re-infiltration of local infiltration excess produced higher soil moisture content, lower overall surface runoff and higher drainage in the fully-coupled model. Higher soil moisture values in WRF/WRF-Hydro slightly influenced precipitation and also increased latent heat fluxes. Overall, the fully-coupled model tended to show better performance with respect to observed precipitation while allowing more water to circulate in the modeled regional water cycle thus, ultimately, modifying long-term hydrological processes at the land surface. This article is protected by copyright. All rights reserved.
Hydroclimatology provides a systematic structure for analysing how the climate system causes time and space variations (both global and local) in the hydrologic cycle. Changes in the relationship between the climate system and the hydrologic cycle underlie floods, drought and possible future influences of global warming on water resources. Land-based data, satellite data, and computer models contribute to our understanding of the complex time and space variations of physical processes shared by the climate system and the hydrologic cycle. Blending key information from the fields of climatology and hydrology - which are not often found in a single volume - this is an ideal textbook for students in atmospheric science, hydrology, Earth and environmental science, geography, and environmental engineering. It is also a useful reference for academic researchers in these fields.
The analysis of land-atmosphere feedbacks requires detailed representation of land processes in atmosphericmodels.The focus here is on runoff-infiltration partitioning and resolved overland flow. In the standard version of WRF, runoff-infiltration partitioning is described as a purely vertical process. InWRF-Hydro, runoff is enhanced with lateral waterflows. The study region is the Sissili catchment (12 800 km²) inWestAfrica, and the study period is fromMarch 2003 toFebruary 2004. TheWRF setup here includes an outer and inner domain at 10- and 2-kmresolution covering the WestAfrica and Sissili regions, respectively. In thisWRF-Hydro setup, the inner domain is coupled with a subgrid at 500-mresolution to compute overland and river flow. Model results are compared with TRMM precipitation, model treeensemble(MTE) evapotranspiration, ClimateChange Initiative (CCI) soil moisture,CRUtemperature, and streamflowobservation. The role of runoff-infiltration partitioning and resolved overland flow on land-atmosphere feedbacks isaddressed with a sensitivity analysis ofWRF results to the runoff-infiltration partitioning parameter and a comparisonbetweenWRFandWRF-Hydro results, respectively. In the outer domain, precipitation is sensitive to runoff-infiltrationpartitioning at the scale of the Sissili area (∼1003 100 km²), but not of area A(5003 2500km²). In the inner domain,where precipitation patterns are mainly prescribed by lateral boundary conditions, sensitivity is small, but additionallyresolved overland flowhere clearly increases infiltration and evapotranspiration at the beginning of thewet season whensoils are still dry. The WRF-Hydro setup presented here shows potential for joint atmospheric and terrestrial waterbalance studies and reproduces observed daily discharge with a Nash-Sutcliffe model efficiency coefficient of 0.43.