Prediction in Ungauged Basins: Approaches for Canada’s Cold Regions
A FRAMEWORK FOR HYDROLOGICAL MODELLING
IN MAGS
E.D. Soulis1, N. Kouwen1, A. Pietroniro2, F.R. Seglenieks1, K.R. Snelgrove3,
P. Pellerin4, D.W. Shaw5 and L.W. Martz5
1Department
of Civil Engineering, University of Waterloo, Waterloo, ON N2L 3G1
Water Research Institute, Environment Canada, Saskatoon, SK S7N 3H5
3Department of Civil Engineering, University of Manitoba, Winnipeg, MB R3T 2N2
4Recherche Prévision Numérique, Environment Canada, Dorval, PQ H9P 1J3
5Department of Geography, University of Saskatchewan, Saskatoon, SK, S7N 5A5
2National
ABSTRACT
There is a strong global research effort in coupling atmospheric and hydrological
models for improved hydrological flow modelling and improved atmospheric
simulation. The land surface is an important hydrological control as it is the
primary influence in the surface-water budget, and it is almost always a
requirement in the implementation of either hydrological or atmospheric models.
Sophisticated soil-vegetation atmospheric transfer schemes also known as landsurface schemes (LSS) are currently being implemented in global climate
models, regional climate models and day-to-day operational forecasting
numerical weather prediction models. Rarely have these been incorporated into
hydrological models. Over the last 10 years, there has been a systematic attempt,
through collaborative research in Canada and under a variety of research
programs, to couple atmospheric and hydrological models using the LSS as the
common link. Our approach has been to combine LSS with hydrological
streamflow models to provide stand-alone hydrology-land-surface schemes
(H–LSS). These stand-alone models are also incorporated as the LSS in the
atmospheric models, creating a fully coupled system. The ability and flexibility
of this system permits the analysis of sensitivities of H-LSS to parameterization
and physical conceptualizations, and the models impact on hydrological and
atmospheric prediction.
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RÉSUMÉ
À l’échelle mondiale, des efforts de recherche importants sont consacrés au
couplage des modèles atmosphériques et hydrologiques pour la modélisation
améliorée des débits hydrologiques et la simulation atmosphérique améliorée.
La surface terrestre est un important déterminant hydrologique en ce sens qu’elle
exerce la principale influence sur le bilan hydrique de surface et qu’il est presque
toujours exigé d’en tenir compte dans la mise en œuvre des modèles
hydrologiques ou atmosphériques. Des mécanismes évolués de transfert entre
sol, végétation et atmosphère, également appelés en anglais land-surface
schemes (LSS) sont actuellement mis en œuvre dans des modèles de climat du
globe, des modèles de climat régional et des modèles numériques opérationnels
de prévision météorologique au jour le jour. Ces modèles ont rarement été
incorporés aux modèles hydrologiques. Au cours des 10 dernières années, il y a
eu une tentative systématique, dans le cadre des travaux de recherche concertée
au Canada et d’une variété de programmes de recherche, de coupler les modèles
atmosphériques et hydrologiques à l’aide des LSS à titre de lien commun. Notre
approche consistait à combiner les LSS aux modèles des débits hydrologiques
pour fournir des mécanismes hydrologiques autonomes de transfert vers et
depuis la surface (en anglais, hydrology-land-surface schemes (H-LSS)). Ces
modèles autonomes sont également incorporés à titre de LSS dans les modèles
atmosphériques, créant ainsi un système entièrement couplé. Les capacités et la
souplesse de ce système permettent l’analyse des sensibilités des H-LSS pour le
paramétrage et les conceptualisations physiques, et les modèles ont des
répercussions sur la prévision hydrologique et atmosphérique.
INTRODUCTION
The recently announced IAHS Decade on Predictions in Ungauged Basins (PUB)
(Sivapalan et al., 2003), initiated in the Spring of 2001, is aimed at achieving
advances in the capacity within the hydrological community to make predictions
in ungauged basins. The overall objective of PUB is to identify the key gaps in
our knowledge that limit our capacity to generate reliable predictions in ungauged
catchments. This includes improved understanding of the water and energy
balance at a number of scales. In data-sparse areas, this poses a particularly
difficult challenge, and modelling plays an essential role by providing a
physically consistent framework for the integration of observations into
descriptions of the balances. The particular challenge of PUB is that not only are
the existing hydrological databases extremely limited, but also many processes
within the water and energy cycles on the earth’s surface still remain poorly
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understood. Therefore, any modelling effort within the context of PUB must be
as comprehensive and systematic as possible, involving atmospheric and
hydrological models that are tightly integrated and that use all available data.
The land surface is an important hydrological control as it is the primary
influence in the surface-water budget, and it is almost always a requirement in
the implementation of either hydrological or atmospheric models. Sophisticated
soil-vegetation atmospheric transfer schemes, also known as land-surface
schemes (LSS), are currently being implemented in global climate models,
regional climate models and day-to-day operational forecasting numerical
weather prediction models. Rarely have these been incorporated into
hydrological models. Over the last 10 years, there has been a systematic attempt
within the Mackenzie GEWEX Study (MAGS) (Stewart et al., 1998) in Canada
as part of a strong global research effort to couple atmospheric and hydrological
models using the LSS as the common link. This paper describes the evolution of
such a system for the Mackenzie GEWEX Study (MAGS). Our approach has
been to combine LSS models with hydrological streamflow models to provide
stand-alone hydrology-land-surface schemes (H-LSS). These stand-alone
models are also incorporated as the LSS in atmospheric models, creating fully
coupled systems. The ability and flexibility of these systems permits the analysis
of sensitivities of H-LSS to parameterization and physical
conceptualizations, and the models impact on hydrological and atmospheric
prediction. For these reasons, we consider this as an appropriate conceptual
framework for PUB modelling activities in Canada.
THE MACKENZIE GEWEX MODELLING FRAMEWORK
MAGS is a major Canadian scientific initiative with the goal of closing the water
and energy budget within the Mackenzie River basin. A major component of
MAGS was an integrated modelling initiative that involved coupling atmospheric
and hydrological models. One of the challenges (Pietroniro and Soulis, 2003)
was to resolve the significant difference in spatial scales, domains, and landsurface representations that the two modelling systems typically use.
Atmospheric models typically are applied continentally or globally, use grid
elements ranging in size from 25 to 250 km, and use simple terrain models;
hydrological models are more regional in nature and use computational elements
that range from 10’s of metres to 10’s of kilometres. Physical aspects, such as
hydraulic cross-sectional information, land-surface slopes and major waterstorage elements such as small lakes or glaciers, are rarely considered in
atmospheric model formulations.
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Another challenge in integrating hydrological and atmospheric models has been
to resolve the differences between the two modelling traditions. As noted by
Pietroniro and Soulis (2003), heterogeneity in the landscape has forced
hydrologists to conceptualize the physics and to seek effective parameter
values. Hydrologists traditionally have used hydrographs to permit the
development of simple objective functions to describe a complex terrain. The
relatively simple hydrological comparison between observed and modelled
streamflow, however, can often lead to reasonable simulations yet poor physical
representation and/or parameterization. The atmospheric scientist is dealing
with a simpler medium (air) but requires more complex objective functions.
Hence, hydrologists tend to focus on effective parameters and precise
objectives, while the atmospheric community has precise parameters and fuzzy
objectives. The result has been completely different approaches to parameter
identification and diagnostics.
The MAGS modelling system (Figure 1) was built in stages by combining two
well-established Canadian codes; the first is CLASS, the Canadian Land
Surface Scheme (Vershegey, 1991) and the second is WATFLOOD, a
distributed hydrological model (Kouwen et al, 1993) developed at the
University of Waterloo. CLASS pays great attention to the vertical and waterenergy budget and has an appropriate interface for atmospheric models.
WATFLOOD has a well-developed routing scheme and provides an effective
connection between runoff and the streamflow records. WATLOOD uses the
grouped response unit (GRU) approach for basin segmentation (Kouwen et al.,
1993). The GRU was adapted from the early hydrology storm-water
management models and is very similar to the “tile” or mosaic approach
adopted for atmospheric-land surface model interaction (Bartlett et al., 2002).
The resulting code, WATCLASS, combines these strengths and can be run in
coupled mode with any atmospheric model.
The linking of models was initially based on uncoupled (linked) tests through
simple forcing of WATFLOOD and CLASS using the operational numerical
weather prediction model (NWP), the Canadian Regional Climate Model
(CRCM), and the Canadian General Circulation Model (CGCM) (Snelgrove et
al., 2005). The results are a good first step but can be slightly misleading since
the land surface is treated independently in each model, resulting in
inconsistent basin state variables. Nevertheless, this exercise was important in
order to test the integrity of the modelling database and to identify major
shortcomings in the models. We call this Level 1 modelling. For the next step,
Level 2, the two models share the same land surface scheme but still run
independently. Thus, the physics are common but there is still no feedback.
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Process
Studies
Level
0
Level
1
Level
2
Level
3
GEM/RCM
GCM
GEM/RCM
GCM
GEM/RCM
GCM
GEM/RCM
GCM
CLASS
with
hydrology
WATFLOOD
WATFLOOD
WatCLASS
CLASS
WATFLOOD
CLASS
WatROUTE
Process
Studies
Figure 1. Atmospheric-hydrological coupled modelling strategy for MAGS.
Finally, for Level 3, the models are truly combined in the expectation that there
is improvement in both the estimates of streamflow and of fluxes to the
atmosphere. This three-level framework for coupling, described by Soulis et al.
(2000) and Pietroniro and Soulis (2003), is part of an overall strategy to provide
a consistent and rational approach to coupling atmospheric climate models with
hydrological models.
The Mackenzie River basin is a data-sparse environment. There are
approximately 82 operational stream gauges in the 1.6 million square kilometre
basin and a similar number of synoptic weather stations. Although not typical of
the developed world, this low-network density is characteristic of much of the
globe, hence the approach presented may be very appropriate for PUB. This
gives special importance to work in the Mackenzie River basin, as the
techniques developed will be more appropriate globally than models developed
in data-rich environments.
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A land cover-based approach is essential for modelling a region such as the
Mackenzie River basin. It minimizes the amount of ground-based data required
for model calibration and allows detailed physics in the vertical water budget on
a landscape basis. This is entirely consistent with the mosaic approach currently
being implemented in CLASS, and the GRU approach used in WATFLOOD. An
extensive database has been developed to support this approach. This includes
topography, land-cover and drainage data, as well as a long-term land-surface
water balance for the Mackenzie basin that consists of approximately 10 km
gridded, mean monthly precipitation, evapotranspiration and runoff fields for the
Mackenzie basin.
Another challenge is the northern climate of the watershed. Although many
existing hydrological models have winter components, the parameterizations are
usually not developed well enough to accommodate the extreme northern
conditions in the Mackenzie basin. Also, physiographic considerations such as
permafrost, frozen soils, snow sublimation and snow redistribution are rarely
incorporated in most hydrological models. As noted by Pietroniro et al. (1996),
there are major problems when models are transposed from temperate climates
and are applied to northern environments. The modelling efforts in MAGS
required incorporation of results from hydrological research basins in the
modelling framework. This involved reviewing the research basin results
(Quinton and Hayashi, 2005), and developing parameterizations such that they
are included in the integrated modelling scheme.
The WATFLOOD Grouped Response Unit
Treatment of land covers by WATFLOOD is a balance between the need for
detail associated with physically based modelling and the inevitable complexity
that comes with the detail. Physically based simulation models often require
breaking the watershed down into smaller units to more closely represent the
observed hydrological and hydraulic phenomena. Distributed models are defined
by their ability to incorporate the distributed nature of watershed parameters and
inputs into a modelling framework. Fully distributed models apply detailed
physics in differential form but are too complex and data intensive to solve for
large basins. Lumped hydrological models often lack the detail of physics and
distributed inputs. Two widely accepted distributed model procedures are the
Hydrological Response Units (HRU) approach (Leavsley and Stannard, 1990)
and the GRU approach. An HRU is a real element within a basin where the
hydrological properties are definable and would not be significantly different if
a smaller scale of discretization was used. This approach is appropriate for small
basins and grids since it is data and CPU intensive. An approach more suitable
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Figure 2. The GRU approach to basin discretization used in WATFLOOD and WATCLASS. The
shading with each circle represents the land-cover based GRU within a grid square.
Water is routed along the stream in the down-slope direction.
for large basins is the GRU, which is a grouping of all areas with a similar land
cover (or other attribute) such that a grid square will contain a limited number of
distinct GRUs. Runoff generated from the different groups of GRUs are then
summed together and routed to the stream and river systems (Figure 2). Two
GRUs with the same percentages of land-cover types, rainfall, and initial
conditions will produce the same amount of runoff regardless of how these landcover classes are distributed. The major advantage of the GRU approach is that
it can incorporate the necessary physics while retaining simplicity of operation.
The surface-water budget in WATFLOOD is computed for each GRU within a
grid square. Infiltration schemes use the well-known Green-Ampt approach.
When the infiltration capacity is exceeded by the water supply, and the depression
storage has been satisfied, the model then computes overland flow from the
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Manning equation. Infiltrated water is stored in a soil reservoir referred to as the
Upper Zone Storage (UZS). Water within this layer percolates downward or is
exfiltrated to nearby water courses using simple storage-discharge
relationships. All GRUs within a grid contribute to the shared lower zone
storage (LZS). An initial LZS is determined through trial and error by matching
stream baseflow at the outlet to the observed hydrograph. Ground water, or
LZS, is replenished by recharge from the UZS according to a power function. A
ground-water depletion function is used to gradually diminish the base flow.
There is only one LZS for each grid. Flow rates through soil depend upon the
hydraulic conductivity that is optimized on the basis of land cover. The total
inflow to the river system is found by adding the surface runoff and interflow
from all GRUs in a grid and the base flow. These flows are added to the
channel flow from upstream grids and routed through the grid to the next
downstream grid using a surrogate channel system with a storage routing
technique.
The CLASS Mosaic
The development of CLASS began in 1987 in response to the perceived need for
second-generation land-surface modelling within the Canadian GCM. The
following synopsis is based on Verseghy (1991) and Verseghy et al. (1993).
Land cover is currently dealt with using a patched land-cover, or mosaic, approach.
Each modelled grid cell can have up to four sub-areas representing bare soil,
vegetation-covered, snow-covered and snow-and-vegetation covered “patches” of
the landscape. Inputs of meteorological variables at the bottom of the atmosphere
are used to drive the energy and moisture balances for each of the sub-areas, and
the resulting fluxes to the atmosphere are passed back to the host
atmospheric model. The surface-energy balance is solved for each sub-area by
expressing the various fluxes as functions of a single unknown, the surface
temperature (of the vegetation, snow or soil as appropriate), and then solving
iteratively.
The soil column is divided into three layers of 0.10 m, 0.25 m and 3.75 m. The
layer temperatures and liquid and frozen moisture contents are carried as
prognostic variables, which are stepped forward in time on the basis of the
energy and moisture fluxes at the top and bottom of each layer. The energy
fluxes are obtained in the course of the solution of the surface energy balance.
The moisture fluxes are calculated using Darcy’s equation (in the case of
gravitational drainage) and the Green-Ampt method (in the case of infiltration).
If the infiltration capacity is exceeded, water is allowed to pond on the surface
up to a maximum surface detention capacity that varies according to land cover.
Ponded water is retained on the surface until it either infiltrates or evaporates.
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The soil albedo and the soil thermal and hydrological properties vary according
to texture and moisture content.
When snow is modelled, it is analogous to a fourth, variable-depth “soil” layer.
As in the case of the soil, the energy fluxes at the top and bottom of the
snowpack are obtained in the course of the solution of the surface-energy
balance. The snow albedo and density vary with time according to simple
exponential decay functions. Snowmelt is modelled as occurring if either the
surface temperature or the average layer temperature is projected to rise above
0°C. In this case, the excess energy is used to melt part of the snowpack and the
temperature is set back to 0°C. Meltwater percolates into the pack and refreezes
until the temperature of the snowpack reaches the freezing point; any further
melt is then allowed to reach the soil. The snow cover is assumed to be complete
as long as the modelled snow depth does not fall below 0.10 m. When this
occurs, the depth is reset to 0.10 and a fractional snow coverage is calculated
through the conservation of snow mass.
The vegetation properties are determined based on the vegetation types present
over the grid cell in question, each of which is assigned to one of four major
groupings: coniferous trees, deciduous trees, crops and grass. The leaf-area index,
roughness length, areal mass and rooting depth of each of these groups are
considered as varying in a distinctive way over seasonal timescales. The canopy
gap fraction, the ratio of the roughness lengths for heat and momentum, and the
diurnal variations of albedo and transmissivity are also different for the four
groups. The canopy interception capacity is calculated as a function of the leafarea index. Rain or snow that does not fall though gaps in the canopy fills the
interception store until the capacity is exceeded. At this point any excess is allowed
to reach the ground. Transpiration is controlled by the bulk canopy stomatal
resistance that is, in turn, a function of leaf-area index, incoming solar radiation,
atmospheric vapour pressure deficit, temperature and soil moisture tension.
Model Integration: WATCLASS
Coupling WATFLOOD and CLASS was not straightforward. Both models are
land covered-based, and have similar land-cover categories, but the treatments
of mixed covers are significantly different. For example, each blends different
state variables within an element, and many of the parametizations are slightly
different. The combined model was constructed using CLASS for the vertical
processes and WATFLOOD for the horizontal process. In essence, the generation
of runoff was assigned to CLASS and the routing was carried out by
WATFLOOD. CLASS was called multiple times to simulate the GRU approach.
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Most of the difference between the models is in the treatment of the soil
moisture budget, and readers are directed to Soulis et al. (2000) for the details
of the approach developed for combining the models. The principles are based
on the topographic index approach that focuses on the near-surface water
balance of valley slopes (Beven and Kirkby, 1979). Version 2.6 of CLASS has
no horizontal drainage (Figure 3a) resulting in the top soil layer remaining wet
too long after a rainfall event. This causes evapotranspiration to be overestimated between runoff events and infiltration to be under-estimated during
these events. To address this, physically based transfer functions were included
in the WATCLASS model to allow movement of water from the soil column
into the micro-drainage system in a physically realistic manner. This is depicted
in Figure 3b. A fundamental drainage element is conceptualized within the
WATFLOOD and WATCLASS framework as a grid element. The element can
be viewed as an assembly of sloped blocks, each with three soil layers and each
with a connection to the drainage system (Figure 4). One of the relevant
morphological properties is LS, the typical length of a block within the element
that supplies, or has the potential to supply, a segment of a receiving stream of
length LV. This can be determined from the drainage density, DD, of the
element, defined as ∑LV/A where A is element area (Soulis et al., 2000).
Drainage density is landform dependent and typically ranges from 2/km to
100/km (Dingman, 1994). LS is the average distance from the divides in the
micro-drainage system to the stream channels and is equal to 1/2DD (Dingman,
1978). This distance represents the ‘slow’ portion of the water-flow system, via
overland routes or through the soil matrix. One limitation is that WATCLASS
requires that a stream element be present within each grid unit. Also relevant is
ΛI, the typical valley slope, called internal slope in order to distinguish it from
the overall slope of the element. ΛI provides the topographic gradient for flow
from the soil blocks. Thus an element has a mosaic of sloped tiles, with average
dimensions LS and LV and average slope ΛI, drained by a system of micro
channels. Elements are large enough that the majority of the inter-element flow
is channel flow.
As in WATFLOOD, the excess surface water will drain to the micro-drainage
system as overland flow, qover, (Figure 3). WATCLASS overland flow is
represented by Manning’s equation, which is the momentum equation applied to
open-channel flow. The depth of flow at the stream edge will depend on how
much of the slope is contributing to overland flow and how much concentration
is occurring. Since the two factors offset each other, we assume the best estimate
of depth of flow at the stream bank is the average effective depth.
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dp
q over
0
dp
l
0
q over
1
10 cm
z1
z1
2
1
q int
10 cm
2
35 cm
z2
z2
35 cm
3
3
410 cm
z3
q drain
(a)
z3
410 cm
q drain
(b)
Figure 3. Soil moisture and land-surface drainage representation in a) CLASS and b) WATCLASS.
Horizontal near-surface flow, qint, called interflow, will occur through the soil
matrix and the macropore structure, leaving the block through the seepage face
(Figure 3). Interflow is less straightforward but can be considered a major flow
mechanism (Dingman, 1994). The primary interflow mechanism is saturated and
unsaturated matrix flow, enhanced by macropore flow near the surface. Moisture
content typically varies from saturation immediately after a heavy rainfall event,
to the wilting point after a sustained dry period. We can focus our attention on
moisture conditions near or above field capacity. Next, because we expect high
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A
LV
n
KH, KV
LS
I
Figure 4. Conceptualization of micro-drainage within a GRU incorporated in WATFLOOD. The
square grid depicted above represents a WATFLOOD grid square. The slope facets
represent the different GRUs within the grid.
values for KH/KV (where KH is horizontal hydraulic conductivity and KV vertical)
and because naturally occurring slopes generally do not exceed 10%, the DupuitForschheimer (Bear, 1972) approximation is valid, and horizontal flow velocity,
VH, can be calculated using a one-dimensional form of Richard’s equation. We
represent the variation in hydraulic conductivity with the depth exponential
relationship used in TOPMODEL. Furthermore, we extend the relationship to
unsaturated conditions by letting hydraulic conductivity vary with soil moisture
using Clapp-Hornberger soils physics.
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Percolation, qdrain, the downward movement of water from the unsaturated zone
to the groundwater system and ultimately to the river system as base flow, is well
represented by Darcy’s law and is a function of the integrated volumetric soil
moisture in the third soil layer, θ3, and KV (Figure 3).
Physiographic Data
Use of a distributed hydrological model requires a detailed description of
topography, land cover, and sub-basin boundaries of a watershed.
WATCLASS requires that the topography of the watershed be outlined and
that internal physiographic features such as contour density, drainage
direction, channel elevations and densities, in addition to river classifications
be parameterized for each grid. These physiographic parameters are key to
describing the horizontal transfer (routing) of water in the model. These
features are derived using land- cover information, typically from satellitebased imagery and digital elevation information.
In the early versions of WATFLOOD, these parameters were derived manually
for small basins from topographic map sheets. However, it is clear that
repeatable and consistent methods using current GIS and image analysis
technologies are required to automate the process of drainage feature extraction.
Aggregating land cover and digital elevation information at the gridsegmentation level is not sufficient to deal with the sub-grid representation
outlined in the GRU approach. As noted by Shaw et al. (2005), assessing
drainage direction and contributing area in square grid-type models is not easily
achieved using simple averaging techniques. They note that by incorporating
sub-grid elevation information and respecting the true hydraulic pathways,
drainage directions and other physiographic features can be preserved. A series
of algorithms (WATMAP) were developed to provide the required internal
routing parameters required for implementation of the hydrological models.
WATMap
An integral component of the WATCLASS modelling framework is the
segmentation of the basin into gridded computational units. Unlike the
atmosphere, the land surface and drainage network that are subdivided in the
gridding process require location to be explicitly considered in order to maintain
the proper drainage network and sub-grid properties inherent to a specific
location. The segmentation process in these models condenses the data to a
format that preserves all of the input information, but greatly reduces the
memory requirements of programs (Kouwen et al., 1993).
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WATCLASS requires six topographic parameters to be input into the model.
Grid elevation is determined at the mid-point of the main channel within the
grid. The elevation at mid-point is usually interpolated from elevations observed
where contours cross the channel. This elevation is used to estimate channel
slope between grid elements for hydraulic routing. The drainage area estimate
for each grid is required in order to match the drainage area of the grid to what
is physically observed on the land surface. Typically, grid segments that fall
entirely within a gauged catchment will contribute 100% of their area to the
downstream gauge. Grid segments that border two catchments require that a
dominant drainage direction be chosen and assigned to the grid segment, with
the remainder of the catchment being assigned to a neighbouring grid segment.
Both grid area and dominant drainage direction are used in determining the
runoff volumes and the grid segment routing path. The concept is best explained
by Figure 5.
Another important physiographic parameter is the river classification assigned to
the grid. Because the grid segmentation approach assumes at least one dominant
stream must be within the segment, hydraulic properties of river roughness and
a meandering factor are assigned to that grid segment. The values are chosen by
the user, or they can be calibrated using standard calibration techniques outlined
by Kouwen et al. (1993). As part of the river routing algorithm, channel density
is also used and there can be up to five channels of equal size within a grid
segment. This is used because large channels are more efficient at conveying
water than smaller channels for the same runoff production. The final parameter
for the grid segment is the contour density. This parameter represents the land
slope rather than the channel slope for each segment.
For each grid square, WATMAP scans every outside DEM element to determine
if flow is in or out of the grid square. WATMAP determines the main drainage
direction out of the grid square, the midpoint elevation of the main channel and
the size of the main channel. The primary and secondary outflows from each
sub-grid watershed are also recorded; this allows WATMAP to determine the
fraction of the grid that is drained in the main drainage direction. The fraction of
the grid not drained in the main drainage direction is added to the appropriate
neighbouring square. Also derived from the outflows is the number of equalsized channels flowing out of the grid square, another parameter required by
WATCLASS.
Past experience with WATFLOOD indicates that surface flow is calculated better
using the maximum internal slope rather than the average internal slope. As a
result, WATMAP calculates the land-surface slope as the cumulative difference in
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Figure 5. Drainage direction and basin contribution for a grid using the WATCLASS segmentation
method.
elevation between cells for each row and column of the grid square individually,
and the row or column with the highest cumulative difference is identified. This
value is multiplied by a contour factor determined by comparing internal slopes
using the manual method and WATMAP on various sample watersheds.
Meteorological Forcing
WATCLASS requires gridded surface meteorological data to drive its
hydrological calculations. Because WATCLASS resolves the water and energy
budget, it requires hourly precipitation, air temperature, wind speed, relative
humidity, incoming short-wave radiation and net long-wave radiation for each
grid element in the modelling domain. Meteorological forcing data for
hydrological models have been traditionally derived from interpolated measured
station data and measured weather radar; however, more recently Global
Circulation Model (GCM) and Numerical Weather Prediction (NWP) archive
data have been applied (Snelgrove et al., 2005). This relatively new source of
meteorological data has positive implications for both the hydrological and the
atmospheric modeller. For the atmospheric modeller, the climate simulation or
weather forecast is evaluated against streamflow data using a hydrological
model. In these cases the watershed effectively acts as “large rain gauge” even
though the caveats and uncertainties of hydrological modelling must be
considered. For the hydrological modeller, a new source of data becomes
available to drive their model from the atmospheric archive. This offers the
opportunity to model remote watersheds for which no gauge atmospheric data
are available, and the benefit of adding spatial structure to atmospheric forcing
data that is lost in the interpolation of gauge data.
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River Routing with WATROUTE
Routing between grid elements is required to convey the water to the downstream
node or gauge location. The routing of water through the channel system is
accomplished using a storage routing technique, WATROUTE. More sophisticated
routing models are available but the application of such models does not appear to
be warranted. In fact, for large watersheds, differences between the routing
methods may well be smaller than the noise in the data (Kouwen et al., 1993). The
method involves a straightforward application of the continuity equation, where
the flow is related to the storage through the Manning formula. Large lake and
reservoir (bigger than a grid element) routing can be explicitly taken into account
in this routing scheme by assigning contiguous lake-grid segments a reach number.
Once the lakes have been located, the outlets are located at the outlet grid. Water
is then routed through the lakes using a user-specified power function.
With the development of the previously described topographic analysis tools and
appropriate meteorological data sources, the data input framework for WATCLASS
is in place. The framework used to create H-LSS is not limited to WATFLOOD and
CLASS. It is our view that this framework will allow for a number of H-LSS to be
developed and tested at a variety of scales, and to be directly incorporated into
atmospheric models. We expand on this in the next section.
A PROPOSED MODELLING PROGRAM FOR PUB - THE MEC SYSTEM
Future modelling efforts will focus on better modelling structure, physics and
parameterization via improved physical understanding. This includes better
schemes for the interaction of soil moisture and transpiration from vegetation,
optimization of land-surface parameters using hydrographs, and the
parameterization of cold-region processes including permafrost, frozen soil,
snow sublimation and snow redistribution. In order to assist in H-LSS model
testing, a number of research projects are in place. The first of these is a coupler
that allows for off-line testing of H-LSS models, yet provides a consistent
framework for coupled atmospheric/hydrological applications, lending itself
well to the goals of PUB.
The Modelling the Environment Community modelling system (MEC) was
developed by the Meteorological Service of Canada (MSC) in collaboration with
many Canadian groups including universities and the private sector. The main
objective of this project is to optimize research and development (R&D) in
environmental modelling by sharing a unified modelling environment. With
advances in numerical modelling and computer power, we now have the
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Meteorological Data
- Models
- Observations
- Analyses
- Radar - Satellite
HLSS in MEC
2 or 1 way Coupling
Land-surface Models
MEC system
Hydrological Models
Figure 6. A strategy adopted for the Environmental Modelling. The MEC system (Modelling
Environmental Community) includes different environmental models (ocean-ice, landsurface, hydrology that are coupled to an atmospheric model using a systematic
coupling allowing the HLSS to run a different time and space scales than the
hydrological model).
scientific and technical capabilities to build comprehensive environmental
prediction systems, integrating expertise from a wide range of disciplines and
addressing important R&D and operational issues. For the moment, the MEC
system includes one atmospheric model, one ocean-ice model, and two landsurface schemes. The system includes also a coupler (RPN-Coupler (Recherche
en Prévision Numérique)) (Pellerin et al., 2004) which allows it to efficiently
link different MEC configurations or environmental models together. Figure 6
presents this strategy adopted by MSC for the environmental modelling. MEC
allows the hydrology-land-surface model to run independently of the
atmospheric models, enabling hydrologists to test the H-LSS at the basin scale
using observed forcing data within experimental basins.
This coupling system also allows the H-LSS to run at those time and space scales
that are not the same as the atmospheric model, but more appropriate for
hydrological simulation, while still providing two-way water and energy
feedback between the atmosphere and the land surface. This allows for a number
of H-LSS models to be tested in a coupled mode with the atmospheric model.
More importantly, it also allows for off-line testing of the H-LSS at the smallbasin scale where the energy and water budget can be closed experimentally,
thus reducing both parameter and model uncertainty. Findings at the
experimental basin scale (i.e grid square scale in WATCLASS) can then be
translated to larger domains, allowing hydrologists to scale from the small basin
to the larger basin.
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Clearly, modelling and data collection cannot be independent activities. The
need for close integration and continuous feedback between models,
observations and process studies are essential elements to making significant
progress in identifying and reducing uncertainty in prediction of water-cycle
variability. To accomplish this, a number of mechanisms should be designed and
built into any research program. The most important of these is to sustain the
close interaction between modellers and experimentalists -- to develop
performance criteria for observation and process studies, including identifying
the most important measurements to make, specification of the appropriate
spatial and temporal scale for measurements, and recommendations about the
optimal resolution and accuracy of the measurements. Success of the Canadian
MAGS program and the development of the MAGS modelling strategy were
initial attempts at such integration.
FUTURE EFFORTS
Future modelling efforts will focus on the interaction of soil moisture and
transpiration from vegetation as well as optimization of land-surface parameters
using hydrographs. It is evident that applications of LSS models incorporated
into traditional hydrological models are still exploratory and have many of the
same pitfalls of hydrological models developed over the last twenty years
(Snelgrove et al., 2005). The framework described and recent advances with
MEC allow for systematic coupled atmospheric-hydrological model
development. Expertise from both experimental and modelling hydrologists is
essential to the success of this modelling framework. Land-surface schemes
belong in hydrological models, and the framework described allows for
advances in both hydrological and atmospheric modelling, based on sound
experimental data and approaches. Through continual feedback between
research groups, PUB will be able to make steady and systematic progress
toward quantifying and reducing the uncertainty in basin-scale water-cycle
models.
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