Introduction
Global warming has fundamentally affected terrestrial hydrological cycle, as
well as water-related sectors. The intensification of the water cycle leads
to an increase of hydrological extreme events such as flooding and droughts,
which influences the reservoir regulation and flood mitigation, and the
coordination of the water supply for agricultural, urban and environmental
sustainability (Huntington, 2006; Oki and Kanae, 2006). While the mitigation
activities including the reduction of carbon emission will not have a
significant impact on slowing the global warming until a few decades later
due to the inertia of the climate system, the adaptation can be an approach
that reduces the negative effects from climate change in a timely manner
(IPCC, 2014). Nevertheless, a well-planned adaptation cannot be achieved
without a reliable prediction of the future.
In terms of terrestrial hydrology, a basic question is how to manage the
water resources in a way that is adaptive to climate change, especially to the
extreme events (e.g., droughts). In other words, how to predict the future
hydrology at a lead time that is not only long enough for taking an action
but also reliable for an effective adaptation is a big concern both for
science and application. While the decadal hydrological prediction is still
at an exploring stage due to very limited predictability over land, the
seasonal hydrological forecasting has been carried out for about half a
century (Pagano et al., 2004) and is being augmented with physical
hydrological models (Day, 1985; Bierkens and van Beek, 2009; van Dijk et
al., 2013; Svensson et al., 2015) through the Ensemble Streamflow Prediction (ESP)
method as well as climate forecast models (Wood et al., 2002; Luo and
Wood, 2008; Mo and Lettenmaier, 2014; Yuan et al., 2013, 2015a) where the
climate predictions are downscaled to drive the physical hydrological models
and provide the hydrological forecasting (Yuan et al., 2015b).
Statistical, dynamical and hybrid seasonal hydrological forecasting systems
are being developed and implemented by multiple research institutions and
operational centers around the world. For example, a national seasonal
streamflow forecasting service operated by the Australian Bureau of
Meteorology (http://www.bom.gov.au/water/ssf), with a
statistical forecasting method based on the joint distribution of future
streamflow and their predictors such as antecedent streamflow and El
Niño–Southern Oscillation (ENSO) indices, now provides forecasts for over
160 locations including major water storages and river systems across
Australia (Wang et al., 2009). A drought monitoring and hydrologic
forecasting system developed by Princeton University
(http://hydrology.princeton.edu/forecast), which is based on downscaled
climate prediction from the Climate Forecast System version 2 (CFSv2) and a
distributed hydrological model (Luo and Wood, 2008; Yuan et al., 2013),
provides soil moisture forecasts and drought outlook over conterminous US
for up to 6 months and is being augmented with remote sensing
products and multiple climate forecast models to provide seasonal
hydrological forecasting over Africa (Sheffield et al., 2014) and global
major river basins (Yuan et al., 2015a). Besides the statistical and
dynamical forecasting systems, a hybrid system called Hydrological Outlook
UK (http://www.hydoutuk.net) is being developed over Great
Britain by an expert merging of a statistical analogue and persistence
method, the ESP method, and a hydrological modeling system driven by the UK
Met Office climate forecasts.
Similar to the seasonal climate prediction, the initial hydrological
condition (IC) is also an important source of predictability for the
hydrology at seasonal timescale and should be carefully treated in
developing a hydrological forecasting system. Basically, IC of snow controls
the seasonal hydrological variations significantly over the headwater
region of a river basin, especially over high-altitude areas. For instance,
Koster et al. (2010) found that the hydrological simulations with the IC of
snow can explain up to 50 % of the variance for the streamflow over the
western USA in the following 5 months. The importance of snow for
streamflow predictability was also confirmed over European basins
(Staudinger and Seibert, 2014) and global major river basins (Yossef et al.,
2013). As compared with snow, soil moisture has less impact on the
hydrological predictability during the snow melting season but can affect
the predictability significantly during other seasons, where its dominance
can last over 6 months over certain river basins (Mahanama et al., 2012).
In addition, the IC of groundwater is also important during the low-flow
period where the subsurface runoff dominates the streamflow (Paiva et al., 2012).
Information at 12 hydrological gauges and the
Nash–Sutcliffe efficiency (NSE) during the periods of calibration
(1961–1981) and validation (1982–2010). The simulated streamflow is verified
against naturalized streamflow.
Gauge
Latitude
Longitude
Drainage
NSE for
NSE for
(∘ N)
(∘ E)
area
calibration
validation
(103 km2)
Tangnaihai
35.5
100.15
122
0.90
0.87
Xunhua
35.83
102.5
145
0.91
0.88
Xiaochuan
35.93
103.03
182
0.78
0.84
Lanzhou
36.07
103.82
223
0.92
0.91
Xiaheyan
37.45
105.05
254
0.92
0.90
Shizuishan
39.25
106.78
309
0.92
0.89
Hekouzhen
40.25
111.17
368
0.86
0.76
Longmen
35.67
110.58
498
0.83
0.74
Sanmenxia
34.82
111.37
688
0.83
0.77
Huayuankou
34.92
113.65
730
0.85
0.81
Gaocun
35.38
115.08
734
0.84
0.78
Lijin
37.52
118.3
752
0.79
0.71
Locations of meteorological and hydrological stations over the Yellow
River basin.
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Seasonal mean (a–d) 2 m air temperature and
(e–h) precipitation over the Yellow River averaged during 1982–2014. The four
seasons are December–January–February (DJF), March–April–May (MAM),
June–July–August (JJA) and September–October–November (SON).
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To assess the contributions of ICs and meteorological forcings, a
theoretical framework called reverse ESP (revESP) was proposed by Wood and
Lettenmaier (2008). For the ESP method, a hydrological model with realistic
ICs is forced by an ensemble of meteorological forcings resampled from the
history; while for the revESP, the hydrological model is driven by observed
meteorological forcings (a perfect meteorological forecast), with ICs
resampled from the history. Wood and Lettenmaier (2008) applied the
assessment framework over two river basins in the western USA and found that
ICs yield streamflow forecasting skill for up to 5 months over northern
California during the transition period between the wet and dry seasons but
have less impact over southern Colorado basin due to a weaker annual cycle
of precipitation. Since then, the revESP framework has been widely used to
assess the role of ICs at regional to global scales (Li et al., 2009; Koster
et al., 2010; Shukla and Lettenmaier, 2011; Paiva et al., 2012; Singla et
al., 2012; Shukla et al., 2013; Yossef et al., 2013; Staudinger and Seibert,
2014; Yang et al., 2014). However, most assessments did not explicitly
investigate the role of the IC of the surface water state variables in the
streamflow forecasting, where it could be a major source of hydrological
forecast uncertainty over rivers with low slope and large floodplains (Paiva
et al., 2012). In addition, the ICs may have different impacts on the
hydrological forecasting over upper and lower reaches of a large river
basin, which is also important for a coordinated water resource management
across the runoff generation and consumption regimes.
As the first paper of a two-part series, this paper introduces an
experimental seasonal hydrological forecasting system developed over the
Yellow River basin in northern China and investigates the hydrological
predictability across the main stream of the Yellow River. The revESP method
is used to assess the contributions from ICs and meteorological forcings.
The assessments conditional on the surface and subsurface water state
variables, and the dry/wet conditions, are being investigated. Seasonal
hydrological forecasting with multiple climate forecast models will be
evaluated in a companion paper, by comparison with the ESP-based hydrological
forecasting (Yuan, 2016).
Data and method
Data and study domain
The Yellow River is the second longest and the second largest river in
China, with a length of about 5500 km and a drainage area of
7.52 × 105 km2. Figure 1 shows the locations of 324 meteorological
stations and 12 mainstream hydrological gauges within the Yellow River, and
Table 1 lists the latitude, longitude and drainage area for the 12 gauges.
The Yellow River originates from the Qinghai–Tibet Plateau, wanders through
the northern semiarid region including the Loess Plateau, passes through the
eastern low land areas, and finally discharges into the Bohai Gulf (Yang et al., 2004).
The meteorological forcing datasets from 324 meteorological stations are
interpolated into 1321 grids at a 0.25∘ resolution, with a lapse rate
correction for temperature at different elevations. The observations from
three nearest meteorological stations are interpolated to each grid by using
the inverse quadratic distance weighting method. Note that the
meteorological dataset compiled in this study has more regional information
as compared with previous studies that are based on about 100 meteorological
stations over the Yellow River basin (Yang et al., 2004; Cong et al., 2009).
Figure 2 shows the gridded seasonal mean surface air temperature and
precipitation averaged during 1982–2014, indicating a typical monsoon
climate with hot and wet summer, and cold and dry winter. The Yellow River
flows across nine provinces in China, where the upstream section of the Tangnaihai
gauge (Fig. 1) is the headwater region, with a cold (Fig. 2a–d) and humid
climate (Fig. 2e–h). The northwestern region between the Lanzhou and
Hekouzhen gauges (Fig. 1) is a semiarid region, with low rainfall but high
temperature (Fig. 2). The northeastern region between the Hekouzhen and
Longmen (Fig. 1) is located in the Loess Plateau, again a semiarid area.
There are several main tributaries between the Longmen and Sanmenxia gauges,
including the Weihe, Jinghe and Fenhe rivers (Fig. 1). The downstream section of the
Huayuankou gauge is located in the alluvial plain, where the riverbed is
elevated above the adjacent floodplains due to sediment deposition and
man-made levees (Fig. 1).
Flowchart of the experimental seasonal hydrological forecasting system
over the Yellow River.
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Description of the seasonal hydrological forecasting system
Figure 3 shows the flowchart of the experimental seasonal hydrological
forecasting system. The system makes use of the seasonal climate prediction
of precipitation and temperature from multiple climate forecast models
participating in the North American Multimodel Ensemble (NMME) project
(Kirtman et al., 2014), a spatial downscaling and bias correction method
(Wood et al., 2002) that is used to transfer global climate prediction of
meteorological forcings for driving a land surface hydrological model and a
routing model at river basin scale. The soil moisture and streamflow
predicted by the system a few months ahead can be used for decision making
and adaptation to hydrological extremes (e.g., drought) especially for
agricultural sectors. And the Yellow River is in a major farmland region in China with
intensive irrigations, where a dynamical-model-based seasonal hydrological
forecasting system that is targeted for adaptation is quite necessary.
The introduction of the climate prediction part of the system and the
evaluation of the NMME-based seasonal hydrological hindcasts during
1982–2010 will be presented in the companion paper. In this paper,
the establishment of the hydrological part of the forecast system (Fig. 3)
is described. The hydrological modeling part consists of the variable infiltration capacity (VIC; Liang et al., 1996) land surface hydrological
model and a global routing model (Yuan et al., 2015a) regionalized over the
Yellow River. The VIC model version 4.0.5 is used to predict soil moisture
and runoff in this study. It is a semi-distributed, grid-based hydrological
model with a mosaic representation of land cover and soil water storage
capacity. The VIC model is widely used to simulate the large-scale hydrology
in China (Xie et al., 2007; Zhang et al., 2014). The routing model, which is
based on an aggregated network-response-function routing algorithm (Gong et
al., 2009), uses the topographic data to calculate flow velocities both in
the hillslopes and the channels, and translates the runoff from the VIC
model to streamflow at each grid cell and routes the flow into rivers and
finally into the ocean (Yuan et al., 2015a). Calibration of the VIC model
and the routing model is described in Sect. 2.3.
Figure 3 also shows that there is a hydrological post-processing part after
the routing, which is necessary because there are model uncertainties that
cannot be calibrated (e.g., irrigation and inter-basin water diversion that
are neglected in most large-scale land surface hydrological models) and the
errors in meteorological forcings from climate forecast models can propagate
nonlinearly after the terrestrial hydrological processes (Yuan and Wood,
2012). The hydrological post-processing will be used in the companion
paper by matching the predicted streamflow with observed streamflow, while
in this paper the calibration and predictability assessment are based on the
naturalized and simulated streamflow respectively. In other words, this
paper will assess the role of ICs in seasonal hydrological forecasting by
neglecting the errors in calibrated hydrological models, and investigate the
hydrological predictability in a “naturalized” Yellow River without human
interventions. Assessing the naturalized hydrological predictability is the
first step toward establishing an operational hydrological forecasting
system and will also provide a guideline for water resources management
over the Yellow River basin.
Calibration with naturalized streamflow
The Yellow River basin is a heavily managed and intervened basin. As
reported by the Bulletin of Water Resources, the observed annual mean
streamflow at the outlet of the basin (i.e., Lijin station) is about
3.15 × 1010 m3 during 1956–2000, while the annual mean
consumed and inter-basin diverted water is 1.48 × 1010 m3.
These consumed and diverted water is usually neglected in the large-scale
land surface hydrological models, and accounting for them in the model
remains a grand challenge due to limited water resources management data. In
the companion paper, the observed streamflow is used to correct the
model simulations and forecasts for each target month through the
post-processing techniques. However, in this paper, the naturalized streamflow
is used to calibrate the hydrological model and to investigate the
naturalized or unperturbed hydrological predictability in terms of ICs. The
naturalized streamflow is calculated by using the observed streamflow, the
water consumed by agricultural, industrial and civil sectors, and the water
regulated by reservoirs. In this study, the naturalized streamflow datasets
are obtained from the Bulletin of Water Resources (http://www.yellowriver.gov.cn/).
The naturalized streamflow data at 12 gauges (Fig. 1) along the main stream
of Yellow River and the rainfall data averaged over the sub-basins are used
to calculate the runoff–rainfall ratios, and the grid-scale runoff time
series over each sub-basin are then obtained by multiplying the
runoff–rainfall ratios with rainfall time series. For the lower reaches, the
difference in streamflow between the target gauge and the upstream gauge is
used to calculate the runoff–rainfall ratios, with the rainfall selected for
those drainage areas between the two gauges. With the spatially
disaggregated runoff time series, the parameters of the VIC model are
calibrated automatically for each grid cell by using the shuffled complex
evolution (SCE) algorithm (Duan et al., 1994). The VIC model is run
from 1951 to 1981 thousands of times, with the parameters searched by the SCE
algorithm to obtain a maximum Nash–Sutcliffe efficiency (NSE) calculated
between simulated runoff and naturalized runoff during the period of
1961–1981, where the simulations in the first 10 years (1951–1960) are
dropped for spin-up. A similar automatic calibration procedure for the routing
model is also carried out. It should be noted that the naturalized
streamflow may contain errors from the measurement of precipitation and/or
streamflow, and the errors may result in uncertainty in the calibrated
parameters and the hydrological model. In the future, multisource (e.g., satellite
and ground) observations combined with data assimilation techniques will be needed
to quantify such uncertainty.
Similar to Troy et al. (2008), seven parameters of the VIC model, including
the variable infiltration curve parameter (b, with the allowed range
of 0.001–1), maximum baseflow velocity (Dsmax, 0.1–50 mm day-1), fraction of
Dsmax where nonlinear baseflow begins (Ds, 0.001–0.99), fraction of maximum
soil moisture content above which nonlinear baseflow occurs (Ws, 0.2–0.99
and Ws > Ds), depths of the second and the third soil layers
(d2, d3, with the range of 0.1–3 m; note that the depth of the first
layer is fixed at 0.1 m) and the parameter characterizing the variation of
saturated hydraulic conductivity with soil moisture (the allowed range
is 3.1–50), are selected for calibration. After the calibration of the VIC
model at 1321 grid cells over the Yellow River, the simulated runoff with
the optimized VIC parameters is used as the input for the routing model, and
the flow velocities over the hillslope and within the channel are selected
for calibration, with the allowed range of 0–1.0 and 1.0–3.0 m s-1 respectively.
Table 1 lists the NSE calculated by using monthly naturalized and simulated
streamflow during the calibration and validation periods, and Fig. 4 shows
the time series of the streamflow at five selected gauges. A NSE value of
1 indicates that the model simulates the reference streamflow perfectly,
and a value below zero indicates that the simulated streamflow is worse than
the climatology. Across 12 gauges, the averaged NSE values during the
calibration and validation periods are 0.86 and 0.82, with a range of
0.78–0.92 and 0.71–0.91 respectively (Table 1). This indicates that the
calibrated hydrological simulation system captures the variations of the
naturalized streamflow over the Yellow River basin quite well, which is also
shown in Fig. 4. However, Fig. 4 also shows that the modeling system
underestimates the high flow at upper reaches (e.g., Tangnaihai) and
overestimates the low flow at middle and lower reaches (e.g., Hekouzhen,
Huayuankou, Lijin) of the Yellow River. The underestimation of high flow
upstream section might be due to the deficiency in the snow-melting module since
the headwater region is located in a cold and mountainous area, while the
overestimation of low flow might be related to the uncertainties in the
subsurface hydrological processes as well as the transport of surface water.
Naturalized (blue) and VIC-simulated (red) monthly streamflow
(108 m3 s-1) at five hydrological gauges located from
upper to lower mainstream of the Yellow River. The solid and dashed lines in
the right panels represent the climatologies during the calibration
(1961–1981) and validation (1982–2010) periods.
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The root mean square error (RMSE) ratio (RMSEESP / RMSErevESP)
as a function of start month and lead time at 12 hydrological gauges. The
RMSE is calculated between the streamflow from a continuous simulation (with
accurate initial condition and meteorological forcing) and that from the ESP
or revESP experiments.
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Experimental design
With the calibrated hydrological simulation system, a set of numerical
experiments are carried out to investigate the role of the initial
hydrological conditions (ICs) in the seasonal hydrological forecasting:
(1) a continuous simulation from 1951 to 2010 is used to generate the ICs at the
beginning of each calendar month and the reference streamflow and soil
moisture for the assessment of the naturalized hydrological predictability
over the Yellow River; (2) the Ensemble Streamflow Prediction (ESP)
simulations initialized at the beginning of each calendar month during
1982–2010, with ICs taken from the same date of experiment (1) and
28 realizations of 6-month meteorological forcings taken from the same period
of the target year while excluding the target year. For example, for the ESP
simulation starting in March 1983, the ICs are exactly the same as the
experiment (1) on March 1983, and the 28 ensembles of meteorological forcings
are those in the experiment (1) during the March–August of 1982, 1984,
1985, …, 2010, without using the forcings in the target year; (3) the
reverse ESP (revESP) simulations similar to the experiment (2), with the
simulations driven by the same meteorological forcings taken from the
experiment (1) during the target year but 28 ensembles of ICs taken from
different years excluding the target year. For example, for the revESP
simulation starting in March 1983, the meteorological forcings are those
in the experiment (1) during the March–August of 1983, while the
28 ensembles
of ICs are taken from March of 1982, 1984, 1985, …, 2010,
without using the ICs in March of the target year (i.e., 1983).
In this paper, all the analyses are based on the ensemble means of the
realizations from ESP and revESP. The root mean square error (RMSE) for ESP
and revESP for each calendar month are calculated by using all 6-month
simulations starting in the same calendar month during 1982–2010. And the
RMSE ratio, which is defined as RMSEESP / RMSErevESP, is used to
assess whether the ICs or the meteorological forcings are more important in
the prediction of streamflow and soil moisture. If the ratio is lower than
1, the ICs prevail over the meteorological forcings in the predictability
of the target hydrological variable (e.g., streamflow or soil moisture) – and
if the ratio is larger than 1, then the meteorological forcings are more important.
Results
Predictability of streamflow
Figure 5 shows the RMSE ratio for different calendar months and lead times
at 12 selected hydrological gauges from upstream to downstream of the
Yellow River basin. For example, the blue line starting in January and
ending in June in Fig. 5a shows that the RMSE of streamflow from ESP
simulation is lower than the revESP simulation in January and February,
indicating that the ICs prevail over the meteorological forcings in the
streamflow predictability during the first 2 months; the RMSE ratio is
larger than 1 from April to June, which suggests that the meteorological
forcings are more important for the streamflow prediction after the first
3 months. In general, there is a gradual increase in the lead time where
the ICs significantly contribute to the streamflow predictability (RMSE
ratio less than 1) from upstream to downstream gauges. From the Tangnaihai
gauge to Shizuishan gauge, the influence of ICs could not persist for 1
month for the forecasts starting in spring or early summer (green lines in
Fig. 5a–f). However, from the Hekouzhen gauge down to Lijin gauge, the ICs
significantly contribute to the streamflow predictability during the first
month for all calendar months (Fig. 5g–l).
From the gauge at the headwater region to that at the outlet of the Yellow
River basin, ICs significantly contribute to the streamflow predictability
for up to 2–5 months for the forecasts initialized in fall and winter, and
the meteorological forcings prevail over the ICs in the predictability after
the first month for the forecasts initialized in spring and summer (Fig. 5).
This indicates that the ICs have stronger control on the streamflow
predictability during the dry seasons than that during the wet seasons. An
interesting feature is that ICs have the weakest control on the streamflow
forecasts starting before the rainy season (May in Fig. 5), which suggests
that the memory of the terrestrial hydrological system drops to the lowest
level at the end of the dry season. This is similar to the results of the
predictability of soil moisture and runoff over the river basins with strong
seasonality (Shukla and Lettenmaier, 2011), where the ICs have the strongest
and weakest control at the end of rainy season and dry season respectively.
The same as Fig. 5 but for the ESP simulations without the initialization
of the routing model.
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The same as Fig. 5 but for those years with streamflow percentiles at
the start month lower than the 20 %.
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For the ESP results shown in Fig. 5, both the state variables for the
surface water and subsurface water are set to the realistic values according
to the continuous offline simulation driven by observed meteorological
forcings. To distinguish the relative importance from different sources of
water storage, an additional experiment is conducted by setting the surface
water state in the routing model to that used in the revESP experiment: the ICs of surface water in the ESP experiment are replaced with the
climatology values. The RMSE ratios of the ESP without the initialization of
the surface water over that from the original revESP are then calculated
similarly, and the results are shown in Fig. 6.
The impact of the initialization of the routing model is less obvious in the
headwater region (e.g., Fig. 6a) given a smaller drainage area and a
shorter travel time. When it goes to the downstream gauges, the RMSE ratios
in the first month increase greatly. As compared with a full initialization
(both the initializations of surface and subsurface water) in the ESP
experiment (Fig. 5), the dominant role of ICs in the first month forecasts
almost disappears for all calendar months (Fig. 6g–l). Nevertheless, the
RMSE ratios for the forecasts beyond the first month do not change, no
matter for the upstream or downstream gauges (Fig. 6). This suggests that
the memory from initial surface water lasts for less than a month over the
Yellow River basin and would not affect the streamflow forecasting at long
leads. However, it is the most important sources of predictability for the
streamflow over a large river basin at a short timescale. The ICs of the
surface water states are essential for a seamless hydrological forecasting
system that aims at integrating short-term flooding forecast to seasonal
drought prediction.
The above analyses are based on the full samples of the hindcasts. To
investigate the role of ICs in the seasonal forecasts of hydrological
extremes, the results conditional on the dry/wet conditions are
investigated. Previous studies found that the ESP approach has low
forecasting skill before the onset of the extreme events (Yuan et al.,
2015a) but can be skillful during the recovery stage (Pan et al., 2013).
Therefore, the forecasts with initial streamflow percentile (according to
the continuous offline simulation) lower than 20 % (or higher than 80 %)
are used to calculate the RMSE ratios, and the drought cases are shown in
Fig. 7. It is found that the RMSE ratios are increasing as compared with
the results of the full samples (Fig. 5). The dominant role of ICs can
persist for 2 months for the forecasts starting in some spring and summer
months at the downstream gauge (Fig. 7l).
The orange lines in Fig. 5 show that the RMSE ratios tend to converge at a
specific target month after the rainy season, regardless of different
forecast lead times. This is because the river basin enters into the
dry seasons where the variability of meteorological forcings becomes
smaller. Such convergence becomes clearer during the drought periods (Fig. 7).
Since the Yellow River has a strong seasonality in the hydro-climate, it
is difficult to recover in a short time once the hydrological drought occurs
at the end of the rainy season. In this case, the influence of ICs persists
for a longer time, and the RMSE ratios do not increase with the increase of
the lead times (Fig. 7). This demonstrates the usefulness of the ESP
approach that is mainly based on the information from ICs in forecasting the
persistency of the hydrological droughts. In other words, the skill of
seasonal climate prediction during the dry season is less important because
the ICs dominate the hydrological predictability for a long time. The result
for the wet cases (initial streamflow percentile larger than 80 %) is
similar, but the impact of ICs lasts for a longer time (not shown). This is
reasonable because wetter ICs usually contain more memory, and the
evaporation process that dries up the soil is a slower process. For the
drier ICs, a single storm may damage all the prior information and the
system becomes less predictable.
To conclude, Fig. 8 shows the maximum lead times (MLTs) where the ICs
prevail over the meteorological forcings in the streamflow predictability
along the main stream and major tributaries of the Yellow River. At the
outlet of the Yellow River, the MLT is less than 2 months during
March–September (Fig. 8c–i) and longer than 5 months during
October–November (Fig. 8j and k), then drops to 4, 3 and 2 months for the
forecasts starting in December, January and February respectively (Fig. 8l
and a–b). This is consistent with the results from a global seasonal
streamflow forecasting at a large river basin scale (Yossef et al., 2013).
Maximum lead time (months) where the initial conditions prevail over
the meteorological forcings (RMSEESP/RMSErevESP < 1) in the
streamflow predictability.
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Moreover, given that the hydrological forecasting system established in this
study can route the runoff and calculate the streamflow grid by grid, Fig. 8
also shows the variability of MLTs upstream and over tributaries.
They generally follow the seasonality pattern of MLT at the outlet, with
longer and shorter values during dry and wet seasons respectively. For the
forecasts starting in November, the MLTs are beyond 5 months except for a
part of the main course in the upstream of the Tangnaihai gauge, and the
main course between the Huayuankou and Gaocun gauges (Fig. 8k). While for the
forecasts starting in May, the MLTs are less than 1 month except for the
main course between the Hekouzhen and Sanmenxia gauges, and that from the
Gaocun gauge down to the outlet. Regardless the tributaries, the biggest
difference in MLT between the lower reaches and upper reaches of the Yellow
River occurs for the forecasts starting in October (the end of the rainy
season), where the difference can be as large as 5 months (Fig. 8j). During
the rainy season, the difference in MLT is about 1 month (Fig. 8f–h).
Predictability of soil moisture
While the change of streamflow is mainly based on fast hydrological
processes including the rainfall–runoff and runoff-routing processes, the
change of soil moisture is much slower due to less conductivity of soil
water. Therefore, the impact of ICs on the soil moisture forecasting is
expected to be more significant than the streamflow. Figure 9 shows the same
MLT plots as Fig. 8 but for soil moisture. Similar to the streamflow
(Fig. 8), the MLT for soil moisture is longer during the cold and dry
seasons and is shorter during the warm and rainy seasons (Fig. 9). However,
unlike the streamflow that represents a basin-scale runoff variability where
the lower reaches are connected with upper reaches, the grid-scale soil
moisture only represents the local variability, and the soil moisture from
upper to lower reaches of the Yellow River has no connections under the
current hydrological modeling framework. In other words, the MLT for the
soil moisture in the lower reaches is not necessarily longer than that in
the upper reaches. As a result, the MLTs for the forecasts starting in
September–February are beyond 6 months in the middle reaches of the Yellow
River due to a dry climate (Fig. 2c), while the MLTs are about 3–5 months in
the upper reaches up to the Lanzhou gauge and the lower reaches between the
Longmen and Huayuankou gauges (Fig. 9a–b and i–l). This pattern holds for
the warm seasons: the ICs prevail over the meteorological forcings in the
soil moisture predictability over the middle reaches for up to 4 months for
the forecasts starting in spring (Fig. 9c–e) and up to 2–3 months for
the summer, while the MLTs are less than 1–2 months over the upper and lower
reaches during the same period (Fig. 9f–h).
The same as Fig. 8 but for soil moisture.
![]()
Differences in maximum lead times (months) between dry years (with soil
moisture percentile lower than 20 %) and the mean results for soil moisture.
![]()
Similar to the RMSE ratio analysis during the dry period (Fig. 7), the
differences in MLTs between the dry cases and the average results (Fig. 9)
are shown in Fig. 10. The soil moisture time series can be converted into
percentiles to form a drought index that is important for the indication of
agricultural drought. In this study, the soil moisture fields from the
continuous VIC simulation driven by observed meteorological forcings are
converted to percentiles grid by grid to identify the local agricultural
drought periods. Again, the ESP and revESP forecasts starting in the dry
years (but the ICs or meteorological forcings from the two experiments are
not necessarily dry according to their experimental design) are used to
compute the RMSE ratios as well as the MLTs.
Figure 10 shows that the MLTs increase by 1 month over most areas. For the
forecasts starting in the summer and early autumn, the increases can reach
2 months over the middle reaches and part of the upper reaches (Fig. 10f–i).
The stronger persistency of the dry soil indicates that the
investment on the seasonal drought forecasting should not neglect the
improvement in the ICs. A 1- or 2-month increase in the forecast lead time will
greatly benefit the agricultural preparedness for the drought events. Given
that the seasonal forecast skill for the precipitation is quite limited
beyond 1 month (Wood et al., 2015), the refinement of ICs through data
assimilation techniques would be very important for the drought forecasting,
especially over the middle reaches of the Yellow River where several main
farmlands exist. The MLTs over the middle reaches during the cold seasons
remain the same because the original MLTs reach the 6-month limit (Fig. 9).
In other words, they may also increase if the ESP and revESP experiments are
carried out to the seventh month or forward. The increases in the MLTs for
the wet cases are more significant (not shown), suggesting that wetter ICs
could dominate the soil moisture predictability longer than drier ICs.
Concluding remarks
This is the first paper of a two-part series on introducing an experimental
seasonal hydrological forecasting system over the Yellow River basin in
northern China. While the companion paper will focus on the
evaluation of the North American Multimodel Ensemble (NMME)-based seasonal
hydrological forecasting (Yuan, 2016), this paper introduces the system and
uses it to investigate the role of initial hydrological conditions (ICs)
over the Yellow River basin.
The forecasting system is similar to the global forecasting system
established by Yuan et al. (2015a) but with a higher resolution and a finer
calibration procedure. Based on 5 decades (1961–2010) of the naturalized
streamflow datasets at 12 mainstream gauges that were recently compiled
by the Yellow River Conservancy Commission, as well as a new forcings
dataset compiled from 324 meteorological stations, a land surface
hydrological model and a global routing model regionalized over the Yellow
River are calibrated grid by grid at a 0.25∘ resolution through an
automatic calibration method. The spatially disaggregated calibration
results in averaged Nash–Sutcliffe efficiency of 0.86 and 0.82 for the
12 gauges during the calibration and validation periods, respectively.
In addition, a hydrological post-processor is used to transfer the
naturalized, simulated or predicted streamflow to those comparable to the
observed streamflow, which is essential for an operational seasonal
hydrological forecasting over the Yellow River where the irrigations and
inter-basin water diversions occur extensively.
By using the hydrological part of the forecasting system, a set of Ensemble
Streamflow Prediction (ESP) and reverse ESP-type simulations that consist of
12 (months) × 29 (years during 1982–2010) × 28 (ensembles) × 6 (forecast leads) × 2 (ESP and revESP) = 116 928 months
model integrations over 1321 grid cells are conducted to investigate
the role of ICs in seasonal hydrological forecasting over the Yellow River.
For the streamflow prediction at 12 mainstream gauges, there is a
gradual increase in the lead time where the ICs prevail over the
meteorological forcings in the predictability. ICs outweigh the
meteorological forcings up to 2–5 months during the cold and dry seasons,
but the meteorological forcings prevail over the ICs in the streamflow
predictability after the first month during the warm and wet seasons. And
from the Tangnaihai gauge at the headwater region down to the Shizuishan
gauge at the middle reaches, the ICs have very limited role (less than a
month) for the forecasts starting before the rainy season.
Given that the ICs of surface water might be an important source of
streamflow predictability, an additional ESP-type simulation is conducted by
setting the ICs of surface water to the climatology. Compared with revESP
simulation, it is found that the initial surface water state is the most
important source of streamflow predictability during the first month,
especially for the downstream areas. However, there is no significant
difference in the streamflow forecasting beyond 1 month regardless of
whether the surface water state is initialized or not, suggesting that other
sources of terrestrial memory such as the snow and soil water storage become
more important for the long-term streamflow predictability.
The role of ICs could be more significant during the dry/wet periods, where
the dominance on the streamflow predictability at the lower gauges can be
extended by a month even in the rainy season. This indicates that the ESP is
a useful hydrological forecasting method after the onsets of the
hydrological droughts or wet spells. The maximum lead times (MLTs) where the
ICs prevail over the meteorological forcings in the streamflow
predictability at the outlet of the entire Yellow River are about 1 month
and 5 months for the forecasts initialized during March–September and
October–November respectively, and they increase from 2 to 4 months for
the forecasts initialized between them. There is a 5-month difference in MLT
between the lower reaches and upper reaches of the Yellow River for the
forecasts initialized at the end of the rainy season, while there is only a
1-month difference during the rainy season.
A similar analysis is applied for the soil moisture, where the MLT for soil
moisture is generally higher than the streamflow. The MLTs for soil moisture
in the middle reaches of the Yellow River are about 6 months during the dry
seasons, and they drop to 2–5 months for the upper and lower reaches.
However, the memory of soil moisture needs to be assessed more objectively
by using in situ and remote sensing observations because currently only the
streamflow observations are used to constrain the hydrological models, where
the soil moisture in the model can only be corrected implicitly based on the
water balance equations.
Although this study has assessed the natural hydrological predictability
that is important for an operational hydrological forecasting with water
allocations and abstractions over the Yellow River, there are a few concerns
that should be addressed in the future: (1) a multimodel framework (Koster et
al., 2010) is necessary to quantify the uncertainty for the assessment of
hydrological predictability; (2) the revESP method only assesses the
theoretical predictability control by using all historical ICs. Actually,
operational forecasters can refine the ICs to some extent before issuing the
forecasts because of the tendency in the ICs (i.e., prior information). In
this regard, the revESP may overestimate the uncertainty in the ICs. On the
other hand, the ESP method may also overestimate the uncertainty in the
meteorological forcings because a conditional ESP method that is based on
certain teleconnections (van Dijk et al., 2013) can be used to select the
meteorological forcings from all historical samples. A more elastic method
that was recently proposed by Wood et al. (2016) could be used to understand
the role of ICs in the seasonal hydrological forecasting with various levels
of uncertainty; (3) the hydrological predictability cannot be fully
understood without combining the hydrological modeling approach and
observation dataset to address different sources of predictability arising
from surface water, soil water and/or groundwater, and the satellite
retrievals of stream stage, soil moisture and terrestrial water storage
would be important for the predictability studies over a large river basin;
and (4) for the river basins with intensive water resources management,
understanding of the naturalized hydrological predictability is just a first
step; more efforts should be devoted to improving the understanding of a
“real” hydrological predictability by incorporating human interventions.
This is also along the line with the Panta Rhei Project, which was proposed by
the International Association of Hydrological Sciences in 2013, to
understand, predict and manage the water systems that are increasingly
impacted by humans, and to provide support for the adaptation to a changing environment.