application of a grouped response unit hydrological model to a northern wetland region

HYDROLOGICAL PROCESSES, VOL. 10, 1245- 1261 (1996)

APPLICATION OF A GROUPED RESPONSE UNIT HYDROLOGICAL MODEL TO A NORTHERN WETLAND REGION

ALAIN PIETRONIRO AND TERRY PROWSE National Hydrology Research institute, Environment Canada, Saskatoon, Saskatchewan S7N 3ff5, Canada

AND LAURENCE HAMLIN, NICK KOUWEN AND RIC SOULIS

Department of Civil Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada

ABSTRACT

Applications of hydrological models to northern wetland-dominated regions have been limited in the past to a few case studies on small basins employing ‘lumped’ models. Only recently have there been attempts to apply the grouped response unit (GRU) distributed modelling approach using terrain classifications to these same basins. This study summarizes recent efforts in applying such a model. For the purposes of implementing the GRU approach, terrain types that are hydrologically significant and characteristic to the wetland-dominated regime were successfully discriminated using a principal component analysis and a hybrid unsupervised/supervised classification technique on Landsat-Thematic Mapper imagery. The terrain classifications were then used as input into a distributed hydrological model for calibration and validation using recorded spring runoff events. Preliminary model applications and results are described. Cali- bration to a historic spring runoff event yielded an r2 value of 0.86. Model validation, however, yielded much poorer results. The problems of model applicability to this region and limitations of sparse data networks are highlighted. The need for more field research in this type of hydrological regime, and associated improvements to the model parameter set are also identified.

KEY WORDS grouped response unit; northern wetland; hydrological modelling

INTRODUCTION

As part of Canada’s contribution to the Global Energy and Water experiment (GEWEX) programme, field research sites have been established in representative regions of the Mackenzie River Basin to study the hydrological processes of cold regions. This paper summarizes modelling research conducted at one of these sites: a vast discontinuous permafrost/wetland zone that feeds the major tributary of the Mackenzie catchment, the Liard River. The area has special hydrological significance because it is the temporal and spatial sequencing of spring runoff from the wetland-dominated basins that determines the timing and severity of the spring ice flood on the Liard River and eventual breakup of the Mackenzie River ice cover. Furthermore, it is river ice breakup on both of these rivers that typically forms the most significant hydrological event of the year, creating water levels that far exceed those which can be produced by equivalent discharge under open water conditions (Prowse, 1986).

The study area is unique hydrologically because it is within a transition permafrost zone characterized by numerous ponds and fens separated by aeolian sand ridges. Moreover, this area is believed to be experien- cing significant climatic warming (Environment Canada, 1995). Based on preliminary field surveys, it appears that many of the hydrological divides located within the sand ridges are permafrost cored. Melting of such divides by climatic warming could lead to drastic alterations of the hydrological response. The greatest impact would be subsurface draining of the ponded water, reductions in surface runoff, increases

CCC 0885-6087/96/ 101 245- 17 0 1996 by John Wiley & Sons, Ltd. Accepted 10 April 1996

1246 A. PIETRONIRO ET AL

in long-term groundwater flow and significant modification of the spring ice flood. The hydrological response of this area needs to be understood to permit assessment of such climate-related effects.

Although considerable research has been undertaken on the spring flood on the Liard River, only preliminary process work has been conducted on the terrestrial runoff mechanisms that control the hydrological response of the wetland catchments (e.g. Craig, 1991; Gibson et al., 1993; Reedyk et al., 1995). A first attempt to conduct meso-scale hydrological modelling of this complex wetland regime is the focus of this paper. Although the long-term objective is to develop process-based algorithms that can be used in full-sea- son meso-scale hydrological modelling of this region, the initial focus is on the critical spring snowmelt period. Specifically, an evaluation was to be made of the suitability of current distributed hydrological models for application in this area and an identification of the major physical processes controlling spring runnoff response.

This initial modelling study decided to use a grouped response unit (GRU) approach. The logic for its selection stems from a recognition that, although new, physically based algorithms are probably required to model the hydrology of this regime effectively, detailed physical models (e.g. SHE: Abbot et al., 1986) are not a practical option because of their numerical/computing limitations and the lack of requisite, representative input data for the region. The GRU approach provides a reasonable compromise because it simplifies the complexity of the hydrological system through a GRU land-class parameterization scheme that still recognizes physical differences in hydrological response and permits the easy incorporation of new process-based algorithms. The SPL7 model (Kouwen et al., 1993) was the specific GRU model selected, primarily because of its detailed overland and streamflow-routing components. These were considered essential for achieving accurate modelling of the rapid spring runoff that characterizes this northern wetland. Another advantage of this meso-scale GRU modelling is that the results can provide the foundation and definition of relevant physical parameters for use in scaling up to, and validating, other GRU models that are being used to conduct macro-scale modelling of the entire Mackenzie River Basin (e.g. Kite et al., 1994).

STUDY SITE

The area of research interest is the remote, undeveloped region of the southern fringe of the discontinuous permafrost/wetland zone (Rennie et al., 1981). An area of approximately 10000km2 near the town of Fort Simpson was selected for specific investigations (Figure 1). This sample subregion has an important advantage in being located close to a class-A synoptic weather station and is in the vicinity of the regional hydrometric survey office, both of which are at Fort Simpson. As a result of this co-location, the Fort Simpson subregion is considered to have the best quality hydrometeorological data set available for this northern regime.

The areal extent of permafrost in this area ranges from 10% in the lowlands to 6O-8O0h in areas of higher elevation. Approximately 90% of the permafrost is associated with a thick organic peat insulating layer, while the remainder is found in well-shaded terrain (Rennie et al., 1981). Vegetation is a primary control of permafrost occurrence. South-facing slopes, colonized by deciduous trees (birch, aspen and alder) tend to be permafrost free and well drained during the summer months. Similar conditions are found on the upland plateaux which contain a mixture of coniferous and deciduous trees and shrubs (e.g. white spruce, aspen, birch and jack pine). North-facing slopes dominated by black spruce are often poorly drained and contain spaghnum-insulated permafrost. Other well-shaded permafrost areas include high-density, stunted conifers with thick organic mats. The low-lying saturated areas, often containing ponded water (Figure 2), tend to be permafrost free. Their stratigraphy usually includes an organic layer of varying thick- ness (0-8m) over a silt-sand layer and an underlying thick, low permeability, clay to silty clay deposit. Bedrock outcrops are primarily limited to several river gorges found near their original confluence with old glacial Lake McConnell (Chatwin and Rutter, 1978). The headwaters of most streams are found within flat to slightly undulating plateaux dominated by bogs, fens and small shallow lakes. Although the descent from the plateaux to the lower lowlands may be relatively steep, the streams tend to meander significantly in their gradual descent to the main Liard and Mackenzie rivers, often through additional areas of fens and bogs.

r

RESPONSE UNIT HYDROLOGIC MODEL

CANADA

Ulmtm 10 0 10 20 30 40 50 60 10 WlM 10 0 10 20 30 40

- - -

Figure 1. The lower Liard River valley study area

1247

1248 A. PIETRONIRO ET AL.

Figure 2. Typical wetland regime showing (a) combination of saturated fens and boreal forest (major swath is from pipeline construc- tion), and (b) characteristic meandering of small streams

Three gauged rivers of varying size but with the best-available hydrometric record (continuity during the spring period) were selected for study in the Fort Simpson subregion: the Martin, Jean-Marie and Birch (Figure 1). Their size and typical spring flow conditions are noted in Table I. As mentioned above, basic climate data are available from the Fort Simpson Airport climate station. Supplemental data are available

GROUPED RESPONSE UNIT HYDROLOGIC MODEL 1249

Table I . Study site basin characteristics*

Basin name Size Number of Mean flow Mean peak flow Peak flow W2) GRUs im3/s) im3/s> (m3/s)

Martin River (1973-1993) 2050 25 20.3 94.1 283.0 Jean-Marie River (1973-1993) 1310 21 11.4 44.0 90.6 Birch River (1973-1993) 542 9 7.0 24.3 81.3

* All statistics are based on daily values and are estimated for the period of record shown for the months of April and May

from a weather station in Fort Liard. Unfortunately, radiation data are unavailable at both sites except for simple sunshine hour data collected at Fort Simpson. Fort Simpson has an average annual temperature of -3.7"C and receives an average of 363mm of precipitation, 40% in the form of snow (Environment Canada, 1993). Historic snow survey records indicate that the average areal mean snow water equivalent (SWE) prior to spring melt is approximately 120mm. Most snowmelt runoff is concentrated in late April to early May, the same time as breakup of river ice. Periods of intense melt can be produced by the strong advection of warm air from the Rocky Mountains and Pacific Ocean to the west which replaces the cold, continental air that typically dominates this interior lowlands region.

HYDROLOGICAL MODEL

The SPL7 model and corresponding user interface, WATFLOOD, is a deterministic, distributed, and unsteady flow model (Kouwen et al., 1993). Originally designed as a flood-forecasting model, it emphasizes those processes most important during periods of significant runoff, including interception, infiltration, interflow, baseflow, overland flow and channel routing.

The SPL7 infiltration component (Philip, 1954) is similar to the Green-Ampt equation except that it also allows for surface ponding. Interception and depression storage are estimated using the equations outlined by Viessman et al. (1977) and Linsley et al. (1949). Snowmelt runoff is derived from a temperature-index approach while the resulting distribution of snow is calculated from landcover-based snow depletion curves (Donald et al., 1995). Overland flow is derived from the Manning equation, while interflow is determined from a simple linear reservoir approach. Streamflow routing is achieved using a storage routing technique.

Table 11. Optimization parameters for SPL7*

Parameter Description Units Lower Upper Reference constraint constraint

Snowmelt M F Melt factor mm/h/"C 0.05 0.5 Anderson, 1973 BASE Base melt temperature "C -0.5 10.0 Anderson, 1973

REC Interflow depletion [-] 0,500 x 0,100 Kouwen, 1995 AK Soil conductivity mm/h 0.040 20.00 Philip, 1954 RETN Upper zone retention mm 0.0 10 0.300 Kouwen, 1995 AK2 Upper zone drainage resistance [-] 0,100 x 0.100 Kouwen, 1995 R3 Overland flow roughness [-] 1.000 10.00 Kouwen, 1995

R2 River channel roughness [-] 0.100 1.000 Chow et al., 1988

PWR Lower zone exponent [-] 0.100 x 0.200 x lo-' Kouwen, 1995 LZF Lower zone function [-] 0.500 2.000 Kouwen, 1995

* f-] Signifies dimensionless parameter.

GRU

Basin

Groundwater


In general, a GRU is a spatial unit of a watershed that can be characterized by spatially constant rainfall and/or snowmelt, and in which runoff generation, overland routing and interflow dominate the response, and the travel times are small. Runoff generation, overland routing and interflow will be different for each of the land cover categories (normally six) within each GRU. These are typically represented as a square grid element as used in this application. Total runoff is summed for each land cover class within each GRU, and routed between GRUs to the basin outlet using a surrogate channel network (Kouwen et al., 1993). The meteorological data required include precipitation, air temperature and snow water equivalent (SWE) distributed for each GRU using a simple interpolation scheme. The vegetation cover in each grid element is usually acquired through the classification of satellite images as detailed below. Vegetation is represented as a percentage of each land class within a GRU. Slope, aspect and elevation are also required for each spatial unit.

The model uses optimization procedures to estimate hydrological parameters that are not easily derived from field or spatial data sources. Table I1 list the parameters used in the SPL7 model along with the upper and lower search limits set during parameter optimization. The values are optimized using the US National Weather Service flow forecasting algorithm (Monro, 1971) and the methodology outlined by Anderson (1973). For a complete description of all parameters used in the hydrological model see Kouwen (1995).

DATA REQUIREMENTS

GRU data and classijication Average elevation, drainage direction and slope data were derived from National Topographic Survey of

Canada (NTS) map sheets for each 10 km2 GRU. Major vegetation-terrain types were obtained from over- lapping Landsat 5 Thematic Mapper satellite images: a full scene from 22 August 1992 and, to encompass all three catchments, a subscene from 16 August 1993. Overlap between the two scenes permitted verifica- tion of the classification scheme which was conducted independently on each image.

The images, as received, are bulk corrected for satellite path distortions and are radiometrically calibrated; however, they do not conform to any existing map projection. The first step in the analysis was to geo-reference the raw image to a universal transverse mercator (UTM) projection using ground control points such as highway intersections, rivers, airports, seismic cut lines, pipelines and small lakes that could be discerned from 1 : 500 000 scale topographic maps and the imagery. The error of each ground control point was estimated before the transformation was applied and those with high errors were discarded or relocated. The resulting root mean squared error was <1.3 pixels (<40 m) in geo-referencing both scenes.

The images were then classified into major landscape categories, ones that could be readily discerned on the image and likely to vary in their hydrological response (Table 111). This classification builds from earlier work by Pietroniro et al. (1995) adapted from Richards (1986). A principal component analysis and combinations of unsupervised and supervised classification procedures were used to obtain final landscape

Table 111. Land classification results

Class description % Cover subscene % Cover full scene

Water cover areas Wetland areas Transitional forest Coniferous forest Mixed forest Deciduous forest Shrub Burned areas Barren (open) Cloud covered

2.18 21.88 27.84 18.00 16.91 10.68 1.23

0.61 0.67

nia

5.21 21.39 53.73 38.61 27.1 1 11.08 4.25 2.88 2.74 nla

Figure 3. Jean-Marie River basin vegetation classification.

GROUPED RESPONSE UNIT HYDROLOGIC MODEL

Table IV. Parameter optimization for 1989 calibration year

1251

Parameter Description Initial value Optimized value

MG (mm/h/”C)

BASE (“C)

AK (mm/h)

REC (-)

R3 (-)

RETN (-)

AK2 (-)

PWR(-) LZF (-)

Snowmelt Mixed Coniferous Transitional Wetlands Mixed Coniferous Transitional Wetlands

Mixed/Deciduous Coniferous Transitional Wetlands Mixed/Deciduous Coniferous Transitional Wetlands Mixed/Deciduous Coniferous Transitional Wetlands Mixed/deciduous Coniferous Transitional Wetlands Mixed/Deciduous Coniferous Transitional Wetlands

Jean-Marie River Martin River Birch River

Groundwater Lower zone exponent Lower zone function

GRU

Basin

0.190 0.190 0.190 0.190

-0.300 -0‘300

1.300 1.300

18.00 18.00 2.000 2.000 0.102 x 0.102 x 0.101 x 0.101 x 8.500 8.500 9.000 9.000 0,280 0.280 0.230 0.230 0.0 10 0.0 10

0.080 x

0-4 0.4 0.4

0.104 x 0.694

0.800 x 1 0 - ~

0.0527 0.0777 0.0657 0.05 15 4.510 6.668 4.400 4.420

0.372 4.200

3.340 0.503 x

0.362 x lo-’ 0.0199 8-010 7.740 2.300 7.980 0.0263 0.02 13 0.0102 0.0103 0.0396 0.0989 0.664 x lo-’ 0.103 x lo-*

14.00

0.509

0.999 0.660 0.533

0.506 x 1.150

categories. The principal component analysis was employed first to remove the correlation between spectral bands on the images. It involves transforming the raw image data into new bands or eigen-channels which are linear combinations of the raw image channels. By reducing the number of raw image bands from six (band 1-5 and 7) to three eigen-channels, the computational time and interband correlation were reduced.

The eigen-channels were then used as an unsupervised classification that uses statistics inherent in the spectral bands to break the image data into distinct common spectral clusters around mean values using the K-means statistical method (Tou and Gonzalez, 1974). This revealed 12 distinct spectral clusters for the full scene and 11 for the subscene. Within selected test zones (training areas), specific vegetation- land types identified on 1 : 25 000 vegetation maps (Northern Pipelines Study, 1974) and 1 : 25 000 timber maps (RR-GNWT, 1995) were associated (supervised classification) with the spectral signatures resulting from the unsupervised classification. These signatures were then used to classify all the pixels in the satellite images (Table 111). A composite image of the Jean-Marie basin using the classified data is shown in Figure 3.

1252

10-

A. PIETRONIRO ET AL.

.,.. , -..

-. I Jean-MarieRiver f ,. . ,

Daily AvaaF TrmpcrPtrrr (Dcg C ) 20 10 0

-10

0 20 40 60

Daily Su&m (Hour) 10 I 1s 10 S 0

0 20 40 NimIbm dhy.

60 80

I ~ ~~

- f w t d ..- cdahted I Figure 4. Results for 1989 using initial parameter estimates


The use of training areas was largely responsible for a highly successful total pixel classification (unsupervised) which reached 91 and 84% for the full and sub scenes, respectively. Areas that did not fit into the classification scheme primarily contained low-density cloud cover but, fortunately, these were outside the boundaries of the three test basins. The Kappa coefficient estimates for the full and partial scenes were 0.914 f 0.003 and 0.964 f 0.002 at the 95% confidence interval. This statistical measure (Rosenfield and Fitzpatrick-Lins, 1982) is an estimate of the difference between the observed accuracy and the probabil- ity of chance agreement of classes. In both cases the coefficient estimates are at acceptable limits (Richards, 1986), resulting in a satisfactory classification for the imagery.

Hydrometeorological inputs Three years of archived data were used in testing of the model; 1989-1991. The calibration year of 1989

represents near average runoff conditions, and the two subsequent years represent slightly lower and higher mean spring flows, respectively. Historic mean and peak flows for April-May are listed in Table I. The SPL7 model requires hourly precipitation and temperature input for the simulation of runoff. These were obtained from archived synoptic information from the climate stations at the Fort Simpson Airport (Figure 1) and from the Fort Liard weather observation station. The Fort Simpson station records hourly data, 24 hours per day. The Fort Liard weather station, located 220 kilometres south-west of Fort Simpson, is the next closest station and provides six-hourly continuous data supplemented by eight hours of hourly data collected during the day. Climate data for each GRU were interpolated from the two stations using weighted averages based on the distance to each GRU. Initial snow water equivalents were also obtained from archived snow survey data. Subsequent snow depletion in the various landscape categories relied on snow cover depletion curves which were derived from 1995 field measurements using the method outlined by Donald et al. (1995).

MODEL APPLICATION AND RESULTS

Model calibration In the initial running of the model, optimization parameters and corresponding limiting values were set

to the standard model values (Table IV), the ones found suitable for modelling runoff events in a more temperate region of southern Canada (Kouwen, 1995). Modifications were made, however, to the original set of vegetation-land classes. These included: elimination of a crop class since the study area includes only undeveloped land; subdivision of a single forest class into mixed/deciduous and coniferous classes; and subdivision of a single wetland class into separate wetland and transitional (wetland to low shrub; intermittent forest) classes. Two river channel parameters were also adjusted to represent better the hydraulic conditions in the study region. River roughness was set to 0.4 for all basins, but a meandering factor was increased from 1.0 to 1.5 to reflect the meandering nature of the test basins.

Figure 4 compares the simulated hydrographs using the initial values for the parameters with measured hydrographs of the three basins. Simulation results are generally poor with a root mean square (RMS) error of the difference estimated as 20.2, 30.2 and 9.3 m3/s for the Jean-Marie, Martin, and Birch rivers, respectively. In addition, there is a lack of agreement on the timing of the peak flow, especially with respect to the Jean-Marie River, which varied by about 20 days.

The next step was to calibrate the model by modifying the snowmelt parameters, BASE (base temperature) and MF (melt factor), which are known to be difficult to transfer in space and time (e.g. Martinec, 1989). The calibration routine used an automatic pattern search optimization algorithm which minimizes the root mean square error between the observed (1989) and predicted hydrographs. Results of the snow parameter optimization are presented in Figure 5. The agreement in shapes of the calculated and measured hydrographs is considerably improved and the RMS error is reduced to 5.2,22*6 and 7.8 m3/s for the Jean- Marie, Martin and Birch rivers, respectively. Notably, the optimized values for BASE are relatively high (Table IV), near the upper limiting temperature of 10°C. This is probably due to the unrepresentative nature of the climate station data. Air temperatures at the climate stations are measured within large, exposed, open sites prone to significant radiative warming, a condition atypical of the wetland-dominated


Daily Avmw Tcmpct.h.r (Dcg C) 20 10 0

-10 -20

0 20 40 60

Daily Smdk (Hour) .)n -- 15 10 J,

0 20 40 60

2s - 20 15 10

- - - mm

40

30

20 i e: lo

0

100

84

20

0 40

0 60

30

6 20 i E

10

0

I Jean-MieRiver 1 -

-

-

20 40 60 84

20 40

1 - ..... CJahbd I Figure 5. Results for 1989 using optimized snowmelt parameters


1 0 - 15 10

5 -

1255

- - .m

20 10 0

-10 -20

0 m 10 60

I D.ilvSuDlhiOc(H~) I

0 m 40 60

40

30

L $ a

10

n

4 0 9 40 60

30

Lo i a

10

n 0 m 40 60

Np.badD.yl

- Ibb*l ..... clloQ*d I I

Figure 6. Results for 1989 using optimized land cover and basin parameters

1256 A. PIETRONIRO ET AL

Table V. Summary of model statistics

River Gauge ~ ~~ ~ ~~ ~~ ~~ ~

Actual Estimate Statistics

Mean Peak Mean Peak rms rms/avg Nash- Peak Volume (m3/s) (m3/s) (m3/s) (m3/s) (m3/s) (-1 Sutcliff ratio ratio

(-1 (-1 (-1 Initial parameters

Jean-Marie River 12.58 36.2 27.37 Martin River 29.63 94.9 43.23 Birch River 10.26 33.2 14.05



Jean-Marie River 2.80 5-56 5.62 Martin River 10.25 35.8 11.47 Birch River 3.67 8.92 5.25


Snowmelt parameters optimized

All parameters optimized

1990 validation with snowmelt parameters optimized

199 1 validation with snowmelt parameters optimized

47.03 75.34 24.14

23.02 40.75 15.1

35.69 65.43 26.26

14.97 33.40 17.9 I

28.65 51.99 19.69

20.2 30.2 9.3

5.25 22.62

7.76

4.78 12.18 4.28

3.74 4.63 3.05

9.30 18.45 4.59

1.61 -1.67 1.02 0.02 0.9 0.23

0.42 0.82 0.76 0.45 0.76 0.46

0.38 0.85 0.41 0.84 0.42 0.84

1.34 -2.53 0.45 0.8 1 0.83 -0.34

0.65 0.37 1.06 -0.17 0.72 0.21

1.3 0.79 0.73

0.64 0.43 0.45

0.99 0.69 0.79

2.69 0.93 2.0 1

0.85 0.90 1.25

2.18 1.46 1.37

0.84 0.6 1 0.6

1-21 0.94 1.02

2.01 1.12 1.43

0.98 1.32 1.15

regime. Subsequent sampling revealed hourly maximum differences of 5- 10°C in air temperature between the airport and the modelling test basins (Hamlin, 1995). Elimination of such base temperature corrections would require air temperature recording within each of the terrain-vegetation classes.

The relatively low melt factors for most of the vegetation-related classes, compared with those for southern Canada, could be due to a number of physical factors. These vary from the large storage potential of the surface ground cover (e.g. depression storage, organic mats, etc.) to atmospheric losses through evaporation/ sublimation. Detailed field studies of the actual melt processes and water fluxes are required to establish the controlling factors and to provide a physically base, narrower range of MF.

The optimized BASE and M F values were then used in a separate run of the 1989 data to optimize the other physical process parameters listed in Table 11. The results of the optimization are shown in Figure 6 and the changes to the parameters in Table IV. This otpimization further improved the simulation, as

Table VI. Snowmelt optimization results for validation years (1990 and 1991)

Land cover 1990 parameter values 199 1 parameter values

Melt factor Base temperature Melt factor Base temperature (M F) (BASE) (MF) (BASE)

Immlhi"C1 I"C1 tmmihi"C1 ["CI

Mixed 0.0524 8.96 0.455 6.13 Coniferous 0.0503 9.49 0.387 5.53 Transitional 0.0500 9-96 0.544 6.27 Wetlands 0.068 1 2.17 0.543 9.60


2 0 - 10 0

1257

- 30

20 , 1s 10 5 0

0 20 40 60

6 4 2 0 .I I I I I- D,

0 20 40 So

10

0

20

1s

8 10 i E

I

0 0

Figure 7. Results for 1990 using reoptimized snowmelt parameters

1258

pGiZGi-1

A. PIETRONIRO ET AL.

0 20 40 60 ~rmrbr crmp

--Apbrl ..--.clbd.M I Figure 8. Results for 1991 using reoptimized snowmelt parameters

80

D


observed by the decreased RMS error (see Table V). Table V summarizes the results of criteria commonly used in watershed model evaluations (ASCE, 1993), such as the Nash-Sutcliffe (or r2) goodness-of-fit. Other criteria include the ratio of observed and estimated peak flows and runoff volumes. In all cases there is a marked improvement in the Nash-Sutcliffe, RMS error and peak statistics. Runoff volume ratios are also slightly improved.

Model validation Model validation was accomplished by applying the calibrated physical parameters to the spring runoff

data for 1990 and 1991. In both cases, the snowmelt parameters were reoptimized because of apparent weakness in their transferability (e.g. Martinec, 1989) or at least until proper values can be defined for this region from field hydrometeorological studies as discussed earlier. The results are given in Table VI. Both years again demonstrate a tendency for the model to assume unrealistically high base temperatures values. The hydrographs for the 1990 and 1991 validations are given in Figures 7 and 8. The Nash-Sutcliffe criteria show a negative r2 (see Table V ) which implies that the estimate is worse than the mean flow for predicting runoff in 1990. Furthermore, even though the 1991 event represents a total flow volume similar to that of the 1989 calibration year, it also has a poor i” and RMS error.

DISCUSSION

In general, calibration of the initial set (southern temperate) of physical parameters for the 1989 event increased the ability of the model to simulate spring runoff conditions. Validation statistics, however, indicate that there are a host of problems with the current version of the model for use in this northern regime, including the lack of representative input data. There are also fundamental problems with the inability of the model parameterization scheme to represent accurately all of the primary runoff-controlling processes of this region. Parameterization improvements require a better definition and understanding of representative values, and may require the addition of new parameters, including ones that must be reinitialized on a seasonal basis with field data. The following discussion summarizes possible model enhancements, along with suggestions for improved data collection.

Snowcover As noted earlier, the melt factors need to be refined for this regime and improvement made to the input

meteorological data set. This includes obtaining a better spatial representation of air temperatures and may include the addition of a radiation term to reflect more accurately the total heat available for melt (e.g. Rango and Martinec, 1995). Air temperature is known to be a poor index of the energy available for melt when solar radiation dominates (e.g. Gray and Prowse, 1992). Importantly, these are melt conditions that frequently dominate this area (e.g. Prowse and Marsh, 1989; Prowse, 1990). Hence, it is expected that modelling of snowmelt in this terrain would benefit from the incorporation of a radiation term, such as that outlined by Brubaker et al. (1996) in this volume. To model this type of terrain properly, data should be collected from each of the basic land classes identified in this study, or methods need to be established to relate snow conditions from index survey sites statistically.

Subsurface $ows and frozen ground Application of the model in temperate regimes has found that successful model validation can be

achieved with initial calibration values for factors affecting subsurface flows, including hydraulic conductivity and interflow rate coefficients. In this application, the calibration values for soil conductivity were found to fall within a realistic range for the relevant soils (e.g. Rawls et al. 1983 : 114mm/hr for sand and 0.3 mm/hr clay soils), and their relative magnitude differed as expected between the different vegetation regimes. Similarly, the magnitude of the interflow coefficient was found (from the calibration run) to be greater for transitional and wetland areas than for deciduous or coniferous forests, indicating that interflow is greater in these regimes, which agrees with field observation. Notably, however, the assumption of constant values for these parameters ignores the combined effect of annual variations in antecedent


moisture levels and frozen ground conditions, factors that are known to affect greatly infiltration and subsurface flow rates. Frozen ground with a high moisture content can greatly impede infiltration, except where macro-pores dominate the flow pathways (e.g. Gray et al., 1985). Hydraulic conductivity needs to be modified to reflect better the full range of values than can occur under frozen and unfrozen ground conditions.

Storage and routing In the case of very cold ground conditions, it is also known that a considerable amount of snowmelt

water can refreeze on or near the ground surface as basal ice (e.g. Marsh, 1990) and not contribute to streamflow runoff until after the entire snowpack has melted. Although this phemomenon is common in northern permafrost regimes, its frequency of occurrence in this permafrost transition zone remains unknown. This needs to be assessed and, if found to be important, incorporated into the model, since this could directly affect the lag in runoff response for those land classes where basal ice develops. A related factor that can also affect the magnitude and timing of delivery of meltwater to the river systems are stream icings, features found to be characteristic of the small streams that drain this wetland system (e.g. Reedyk et a!., 1995). Another major physical process that controls the magnitude and timing of snowmeit delivery to the streams is the water storage within the multitude of bogs, fens and ponds. Surface and subsurface routing can, in many cases, be dominated by the location, extent and retention capacity of these features, including the absorptive power of peat material (e.g. Roulet and Woo, 1986). Unfortunately, the GRU approach does not account directly for changes in storage along flow pathways within a GRU, and struc- tural changes in the model will be required to address this issue.

CONCLUSIONS

To achieve meso-scale modelling of a northern wetland regime, a GRU approach is the only distributed modelling approach that incorporates land class differences into the model parameterization, yet is still numerically efficient at the meso- and macro-scale. It is clear, however, that a host of modifications to existing models like SPL7 are required before simulation of spring runoff can be achieved. To direct such modifications, an improved understanding of the major controlling physical processes must first be acquired. It was possible to calibrate satisfactorily the SPL7 model to predict streamflow using the original set of physical parameters formulated for application in a southern temperate climate. However, validation tests demonstrated that the parameter set had a number of deficiencies for application in a cold, northern, wetland-dominated regime, both in terms of the types of parameters and the range of appropriate values. Improving the suitability of the parameter set will require additional field research to quantify better some of the controlling processes. Given the apparent sensitivity of many of the controlling processes to antecedent moisture conditions (e.g. storage, infiltration and routing processes), it is probable that a significant improvement in modelling accuracy could be achieved by relating some of the parameter values to such a term.

Other obstacles to achieving accurate modelling of this regime are the scarcity and representativeness of available hydrometeorological data. Proper meso-scale modelling of this hydrological system will require augmentation of the existing network which is deficient in both spatial coverage and types of collected data (e.g. meteorological, snow surveys, soil moisture).

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