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Comparison of two daily streamflow simulation models of an alpine watershed
Journal o f Hydrology, 77 (1985) 171--186
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Elsevier Science Publishers B.V., A m s t e r d a m -- Printed in The Netherlands
[1] COMPARISON OF TWO DAILY STREAMFLOW SIMULATION MODELS OF AN ALPINE WATERSHED
C H A R L E S M. B R E N D E C K E 1 , D O U G L A S R. L A I H O 2 and D Y A N C. H O L D E N 1'*
1 Department o f Civil, Environmental and Architectural Engineering, University o f Colorado, Boulder, CO 80309 (U.S.A.) 2 Hydrologic Consulting Engineers Inc., Boulder, CO 80301 (U.S.A.) (Received J u n e 14, 1984; revised and accepted S e p t e m b e r 11, 1984)
ABSTRACT Brendecke, C.M., Laiho, D.R. and Holden, D.C., 1985. Comparison of t w o daily streamflow simulation models of an alpine watershed. J. Hydrol., 77: 171--186. A comparison of daily streamflow simulation by two different c o m p u t e r models has been c o m p l e t e d for an alpine basin in central Colorado, U.S.A. The c a t c h m e n t is part of a 33-km 2 area providing m o s t of the City of Boulder's raw water supply. It is located b e t w e e n 2964 and 4103 m a.s.1, in elevation along the Continental Divide 24 km west of Boulder, Colorado. The S S A R R m o d e l simulation, which makes generous use of lumpedt y p e parameters, was c o m p a r e d with the PRMS m o d e l simulation, which makes greater use of individually defined parameters, by examining their respective ability to fit observed hydrograph volumes and shape. Both c o m p u t e r models were found to simulate streamflow equally well. The PRMS m o d e l was f o u n d to be more suitable overall because of its ability to handle small c a t c h m e n t s and the generous use of individual physicallybased parameters.
1. I N T R O D U C T I O N
Water availability in the western U.S.A. is determined largely by snowmelt runoff from high-altitude source areas of major rivers, Despite the importance of alpine source areas to water supplies, knowledge of the hydrology of these high-altitude catchments is limited. Present methods of supply projection rely principally on snowpack observations at a relatively small number of sites and yield only seasonal volumetric estimates of water availability. The principal reason for the lack of knowledge of alpine hydrology is remoteness and the consequent difficulty of obtaining meteorologic and hydrologic data. As an example, less than a third of Colorado's stream gaging stations are located at elevations of more than ~ 2 5 0 0 m above * Present address: 235 W. Grove, Pomona, CA 91767, U.S.A.
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mean sea level {a.s.1.) while the vast majority of surface water supplies in the State rely on runoff originating at higher elevations. Numerous water utilities (e.g., the City of Boulder) operate reservoir systems in alpine catchments. The growing population of Colorado and the rapidly increasing demand for water make sound management of these alpine watersheds imperative. It is particularly important to have an accurate prediction of the runoff rates in these catchments when drought conditions prevail at lower elevations, since the melting of snow and ice may represent virtually the only source of water available during these times.
1.1. Research approach This paper discusses the preliminary phase of an ongoing research program the aim of which is to better understand the hydrology of alpine watersheds (Brendecke et al., 1984). The results of this ongoing effort are expected to have practical application in the areas of water-supply system development and operation and in assessment of land-use management strategies. A particularly urgent aspect of the latter relates to wilderness access policies devised to cope with the rapidly increasing population along the Front Range urban corridor. The research discussed herein describes the screening and comparative evaluation of candidate hydrologic modeling systems upon which future work might build. The comparative evaluation was made by applying the t w o selected modeling systems to an alpine catchment.
1.2. Study site The study site consisted of a portion of the North Boulder Creek catchment c o m m o n l y known as the Green Lakes Valley. The study site, shown in Fig. 1, covers an area of ~ 7.6 km 2 ranging in elevation from 3274 to 4100 m (a.s.1.). The western boundary of the study site is the Continental D1
lu~ ~
,A CLIMATE STATIO~ ~-', SEGMENTBOUNDARY ~)
SEGMENT NUMBER
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~RIDGE
N SCALE APPROX 1:32000
Fig. 1. Green Lakes Valley study site.
SADDLE
~
SILVER LAKE
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Divide; drainage of the valley is to the east into North Boulder Creek and ultimately to the South Platte River. The Green Lakes Valley was divided into an upper and a lower basin. The upper basin is completely alpine in nature with steep rocky slopes, some permanent snow and ice fields, and low tundra vegetation on the valley floor. The upper basin contains two natural lakes, Green Lakes 4 and 5. The lower basin is more sub-alpine with somewhat flatter hillslopes and heavier vegetation extending to Douglas fir at the lower portions. The lower basin contains four lakes, Lake Albion and Green Lakes 1--3, all of which are artificial and are regulated for water-supply purposes by the City of Boulder. Streamflow-recording stations were established, as shown in Fig. 1, below Green Lake 4 and below Lake Albion. These stations provided a flow record against which the hydrologic models could be calibrated (Caine et al., 1983). Meteorologic data used in the models were obtained from nearby climate stations, also shown in Fig. 1, maintained by the Institute for Arctic and Alpine Research of the University of Colorado (Loslaben, 1983) and by the U.S. National Weather Service. Model calibration essentially consisted of adjustment of model parameters until stream flows predicted using meteorologic data matched the observed flow records.
2. M O D E L S C R E E N I N G A N D S E L E C T I O N
The first step in the research was a review of existing hydrologic modeling systems and programs, the aim of which was to identify those which were potentially applicable to the study site. The initial list of 25 modeling systems was screened to 8, then to 2 (Laiho, 1983). Desirable model characteristics used as screening criteria included the following: (1) use of a continuous daily water balance; ( 2 ) a distributed structure allowing separate representation of watershed segments; (3) reliance on physically-based process descriptions; and (4) use of an energy budget approach in determining snowmelt. The two selected models were the Streamflow Synthesis and Reservoir Regulation (SSARR) model developed by the North Pacific Division of the U.S. Army, Corps of Engineers, and the Precipitation R u n o f f Modeling System (PRMS) of the U.S. Geological Survey (U.S.G.S.). The following discussion describes briefly how each of these hydrologic models works. 2.1. SSARR model characteristics The SSARR model has been under development since 1956; the version used in this study was SSARR-4 which was completed in 1972 (U.S.A.C.E., 1975). A more recent version of SSARR is available, b u t at the time the research was begun, it was not in a form readily compatible with the Cyber ® 1 72 computer used in this study. SSARR has been applied extensively to
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I
PRECIPITATION~
TEMPERATURE SNOW
ACCUMULATIOr
EVAPOTRANSPIRATION
MFLOW
Fig. 2. SSARR r u n o f f synthesis structure.
management of the Columbia River system and to other river systems in the U.S.A. and abroad. The SSARR model has three basic components: (1) a catchment and runoff synthesis component; (2) a river channel routing component; and (3) a reservoir regulation component. The runoff synthesis c o m p o n e n t was the primary object of interest in the comparative evaluation. A schematic showing the structure of the runoff synthesis c o m p o n e n t is shown in Fig. 2. The operating characteristics of the model may be briefly explained by following an element of precipitation input through the model until it becomes streamflow. The amount of precipitation falling on a portion of the
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catchment may be adjusted to reflect differences in elevation, proximity to rainfall gages, and other factors. The form of precipitation (i.e., rain or snow) is determined from daily temperature data and will be discussed later. Net precipitation (which includes rainfall and snowmelt) is divided into runoff, soil-moisture increase and evapotranspiration loss by means of a user-supplied table of soil moisture index (SMI) vs. r u n o f f percent (ROP). The ROP is determined from the SMI at the end of the previous time step and total runoff then computed as ROP times net precipitation. The SMI is then updated by subtracting evapotranspiration and total runoff from the precipitation input and adding the remainder to soil moisture. R u n o f f is next divided into three parts: surface, subsurface and baseflow. Baseflow is computed from a user-supplied table of baseflow infiltration index (BII) vs. baseflow percent (BFP). In a manner similar to that used for total runoff, the BFP is determined from the BII of the previous time period and baseflow computed as BFP times total runoff. The BII is then updated for use in the next time period. The direct runoff which remains after baseflow has been subtracted from total runoff is next separated into surface and subsurface runoff. This is accomplished by reference to another user-supplied table relating surface runoff to direct runoff. This last relationship generally provides for increasing the relative volume to surface runoff in any time period as the volume of total direct runoff increases. The volume allocated to subsurface runoff cannot exceed a user specified maximum rate. The three components of runoff (i.e. surface, subsurface and baseflow) are each then routed through a series of hypothetical storage reservoirs designed to represent the time delay experienced by each on its way to the stream. These storage reservoirs are specified by the user as to number and time of storage in each. Water leaving the last storage reservoir of each component is summed to obtain the total contribution to streamflow for that time period. Several options exist in SSARR to model the accumulation and melting of snow. Those used in this study were the generalized snowmelt equation (GSE) with the snowband option (U.S.A.C.E., 1975). The GSE computes the saowmelt rate for the day as a function of short- and long-wave radiation, and convection--condensation from overlying air. Although based on an energy budget approach, the GSE does n o t provide continuous accounting of the heat content of the snowpack. The snowband option serves two functions in the computation of snowmelt runoff; the first is to determine the form of precipitation and the second is to determine the proportion of the snowpack subject to melting on a given day. Both of these determinations are based on air temperature and segment elevation. As mentioned previously, the SSARR model has two routing components, a channel routing component and a reservoir routing component. Due to the small size of the study site the time delay due to channel flow is negligible and was ignored. Time delay in lakes was dealt with using the reservoir routing c o m p o n e n t which relies on iterative solution of the storage equation.
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2.2. PRMS characteristics The basic rainfall and snowmelt runoff features of PRMS were developed in 1972 for a small Rocky Mountain catchment (Leavesley, 1973). Since adoption by the U.S.G.S., numerous other features have been added. PRMS is now a fairly large modular system consisting of a main calling program (which controls time sequencing) and a library of subroutines which perform calculations representing various hydrologic and hydraulic processes. Usersupplied subroutines may be readily added to the system library. The version of PRMS used in this study was obtained from the U.S.G.S. in late 1982 (Leavesley et al., 1982).
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Fig. 3. PRMS runoff synthesis structure.
177
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k I--
L SUBSURFACE RESERVOIR L ilt L l
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SURFACE RUNOFF
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SINK Fig. 4. Disposition of infiltrated water in PRMS.
The basic rainfall and snowmelt runoff features of PRMS used in the research are shown in Fig. 3. Using the modular structure of PRMS, components representing specific processes can be added or deleted. As before, the operating characteristics of the model may be understood by following an element of precipitation through it. Net precipitation (i.e. that remaining after interception) is first used to satisfy surface r u n o f f in PRMS. The depth of surface runoff is calculated using a contributing area concept. The proportion of the catchment segment which produces surface runoff (as if impervious) varies with the level of storage of water in a "recharge zone". The recharge zone comprises that upper soil layer from which direct evaporation can occur. The upper and lower limits of contributing area and the maximum storage capacity of the recharge zone are user-defined. What remains after subtraction of surface runoff is termed infiltration. The disposition of infiltrated water in PRMS is shown schematically in Fig. 4. Infiltration is first used to bring the upper soil zone (i.e. the soil layer from which evapotranspiration can occur} to field capacity, which is userdefined. Excess water is next used to satisfy a user-defined contribution to the groundwater reservoir and the remainder percolates to the subsurface reservoir. Water in the subsurface reservoir may become subsurface runoff or percolate to groundwater. The subsurface runoff volume and the percolation to groundwater are computed as functions of the a m o u n t of water in the subsurface reservoir. Moisture in the groundwater reservoir may be lost to a user-defined " s i n k " or routed, as a function of level of groundwater storage, to baseflow. Coefficients of all these routing functions are user supplied. The three components of runoff, surface, subsurface and baseflow, are then summed to obtain the total runoff from the catchment segment. Snow accumulation and melt are simulated in PRMS using a continuous water- and energy-balance approach; the water balance is computed daily
178 and the energy balance twice daily. Precipitation is classified as rainfall or snow on the basis of air temperature. The snowpack is represented in PRMS as a two-layer system. The surface layer is subject to heat transfer with the environment and with the underlying pack, while the underlying pack may exchange energy only with the surface layer. The surface layer melts only if there is a net heat transfer to it and if previous heat transfer has brought it up to freezing temperature. The net heat transfer TCAL is computed as: TCAL = CALN + CALD + CALP where CALN = night-period energy balance of long-wave exchanges between air, snow, and vegetation; CALD = day-period energy balance of long-wave exchanges plus short-wave radiation input; and CALP = energy content of precipitation, if any. Melt (which occurs in the surface layer) then adds heat to the underlying pack in the form of liquid water. Only when the underlying pack is brought up to freezing temperature and has reached its liquid water holding capacity does snowmelt reach the soil surface. Snowmelt is used first to satisfy the soft-moisture deficit and continues to infiltrate up to a user-defined maximum rate. Only if this rate is exceeded is surface runoff produced. Infiltrated water is treated in the model as discussed previously. There was no channel routing function in the version of PRMS used in this study. Flow was delayed in lakes by a hydrologic routing m e t h o d similar to that used in SSARR. Storage--outflow relations for each lake or reservoir were user-defined. Because the reservoir routing routines in PRMS were developed for natural lakes, some programming modifications were required to enable the model to simulate operation of the reservoirs in the lower basin.
3. COMPARATIVE APPLICATION Application of the two modeling systems to the study site consisted of four tasks: symbolic representation of the catchment, estimation of catchment parameters consistent with the symbolic representation, development of daily meteorologic data files for use as model input and model calibration. A special effort was made to use consistent symbolic representations and parameters wherever possible in both models to facilitate comparison of results.
3.1. Symbolic representation Both models allow the user to divide the catchment into segments which are hydrologically and/or climatologically similar. The same general scheme of catchment segmentation was used for both models and this scheme is shown in Fig. 1.
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The upper basin of the study site was divided into four segments, based primarily on slope, aspect and water storage capacity (Laiho, 1983). Segment 1, at the head of the valley, is east-facing with little vegetation and several permanent snowfields. Segment 2 is predominantly north-facing with steep, rocky slopes and little vegetation. Segment 3 is predominantly southfacing with somewhat gentler slopes and more tundra vegetation. Segment 4 is the valley floor. It is relatively flat with a fairly well-developed soil profile, a great deal of tundra vegetation, and a significant amount of water storage capacity in the two lakes. The lower basin was divided into five segments, also based primarily on slope, aspect and water storage (Holden, 1983). Segment 5 consists of steep talus slopes with little vegetation and faces northeast. Segment 6 faces mostly south and has fairly steep slopes covered with talus and tundra vegetation. Segment 7 comprises most of the valley floor. It is fairly flat, contains three lakes, and is more heavily vegetated, with shrubs and krummholz. Segments 8 and 9, facing northeast and southwest, respectively, have the best developed vegetation, consisting of gentle slopes with stands of Douglas fir. In both models segments 1--4 were defined to be tributary to the flow recording gage below Green Lake 4, and segments 5--7 tributary to Lake Albion. Segments 8 and 9, along with water spilled or released from Lake Albion, were then made tributary to the flow-recording gage at Albion townsite. The hydrologic processes within each segment are simulated as previously described. 3.2. Estimation o f parameter values
While both models depict the same fundamental processes, differences in the specific relationships used lead to different parameter value requirements. As a result, the discussion below addresses the two models individually. Parameters used in SSARR can be viewed as falling into two general groups, those relating to processing of meteorologic data and those relating to generation of runoff from net precipitation at the ground surface. Initial value estimates for the meteorologic parameters were determined from three general sources of information: (1) the program user's manual and discussions with support personnel; (2) commonly available reference documents on hydrology and snow hydrology (e.g., U.S.A.C.E., 1956, 1960; Chow, 1964; Linsley et al., 1975); and (3)informal discussions with individuals familiar with the watershed combined with site visits and professional judgement. Section 4 of this paper discusses the relative ease or difficulty encountered in estimating values. Some values were subsequently modified during calibration. SSARR parameters relating to runoff generation are lumped, that is, a few gross relationships take the place of many detailed ones. The lumped
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parameters essentially comprise the table of SMI vs. ROP and similar curves discussed earlier. The initial values for the lumped parameter-soil moisture relationships were determined from soil maps and surveys of the area (Moreland, 1960; Retzer, 1962; U.S.D.A., 1975; Burns, 1980). Hydrologic characteristics were determined primarily from Soil Conservation Service runoff curve numbers thought to most closely describe the watershed soil characteristics (U.S.D.A., 1972; Laiho, 1983). As with SSARR, parameter values required by PRMS may also be viewed as relating to meteorology and runoff generation. Initial value estimates for the meteorologic parameters were determined from the same three general sources of information used in SSARR. Some values for PRMS parameters relating to runoff generation could be determined from measurements and observations at the study site, e.g. recession coefficients, soilmoisture holding capacity, and depth of recharge zone.
3.3. Input data requirements Meteorologic input data required by the two models are listed in Table I. Unless otherwise noted, a daily record of each data type is required. Sources of these data were several climatologic stations operated in the catchment and on nearby Niwot Ridge and shown earlier in Fig. 1 (Furbish et al., 1983; Loslaben, 1983). Stations D1 and Saddle are maintained by the Institute of Arctic and Alpine Research of the University of Colorado and provided daily records of precipitation, temperature, humidity and solar radiation. The Silver Lake station within the watershed is operated by the National Weather Service and provided a daily precipitation record. In SSARR, meteorologic input to each catchment segment can be computed internally as a weighted average from many climate stations, the weights being user-defined. In PRMS, precipitation data from up to five climate stations may be used, provided that no more than one station is used per segment. For all other climate data, PRMS relies on use of a single TABLE I Meteorologic data requirements (daily values) SSARR
PRMS
Precipitation Temperature (max.) Solar radiation Basin albedo Dew point Wind velocity .2
Precipitation Temperature (max. and rain.) Solar radiation* 1
• 1 May be computed internally. • 2 Or constant average value.
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station. For comparative purposes the assignment of climate data to catchment segments was the same in both models. 3.4. Calibration
Model calibration essentially involves modification of various parameter values until hydrologic performance predicted by the model more or less matches that observed in the catchment. Measures of hydrologic performance used in this process were runoff volume and hydrograph shape. The calibration process covered the water-year 1982 (Caine et al., 1983). Complete data for water-year 1983 were not available in time to permit discussion herein. Calibration of a complex hydrologic model is largely a trial-and-error process although some automated routines exist (Liou, 1970). It is generally accepted, however, that attention should first be given to parameters affecting runoff volume. Once observed and predicted volumes agree, parameters modifying hydrograph shape can be adjusted (Cundy and Brooks, 1981). Ideally, the calibration should cover several consecutive years to lessen impacts of climatologic anomalies (e.g., heavy late-season snowfall) within a particular year. Where this is not possible, a year close to average, hydrologically speaking, should be used. Based on a comparison of 1982 snowcourse data at the study site and averages from the preceding 15 years, water-year 1982 was only slightly below average (Laiho, 1983). Calibrations of the upper and lower basins of the Green Lakes Valley were done separately (Holden, 1983; Laiho, 1983). A numerical summary of the upper basin results is given in Table II and plots of the observed and predicted hydrographs are shown in Fig. 5a and b. Because the flowrecording station at Green Lake 4 is typically buried by snow and/or frozen from October to June, the runoff volume comparison was based on the period from July through September. As can be seen in the hydrograph T A B L E II Calibration summary Statistic
Streamflow volume as % of o b s e r v e d Daily s t r e a m flow c o r r e l a t i o n coefficient
u p p e r basin
lower basin
SSARR
PRMS
SSARR
PRMS
107 ( 1 1 3 )
91 ( 9 7 )
93
94
0.72 ( 0 . 8 5 )
0.71 ( 0 . 8 7 )
0.91
0.90
N u m b e r s in p a r e n t h e s e s reflect d e l e t i o n o f r u n o f f excess f r o m J u l y 1 9 8 2 s t o r m event.
182 D.4 ~N,3
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Fig. 5, Upper basin calibration hydrographs.
plots, a sizeable portion of the snowmelt runoff may have occurred before the recording gage began operating and the peak runoff may have been missed. This problem may be lessened in water-year 1983 as both flowrecording stations were at least intermittently operable before peak r u n o f f was reached. The sharp spike in observed runoff occurring at the end of July also deserves explanation. A period of three days of more or less continuous rainfall was under-recorded at climate station D1 leading to model underprediction of runoff. This discrepancy could be attributed to gage catch deficiency at D1, extremely localized patterns of precipitation missing DI, and/or to rapid rain-induced melting of isolated remaining snowfields (Laiho, 1983). To assess the effects of this discrepancy on model calibration, Table II also presents numerical summaries calculated neglecting the excess observed r u n o f f during those three days. Numerical summaries and hydrograph plots resulting from calibration of the lower basin are also given in Table II and are shown in Fig. 6a and b, respectively. It should be noted that streamflow entering the lower basin from Green Lake 4 was observed rather than predicted; that is, the models were modified to accept the observed flow record from Green Lake 4 as input. This was done in order to better assess differences in model performance between the essentially alpine upper basin and the more sub-alpine lower basin. The flow-recording gage at Albion town site was operational, at least intermittently, from late May through September, and the summaries in Table II are based on that total period with missing observations filled in by linear interpolation (Holden, 1983). This permitted model calibration to encompass a portion of the rising limb of the snowmelt hydrograph not possible in the upper basin. Also, because the climate stations used in the lower basin were different from the upper basin, the discrepancy due to the late July rainstorm is less evident. The rather extreme fluctuations in flow predicted by SSARR in July and August appear to be the result of the reservoir routing algorithm used in the model. Further calibration was ineffective in mitigating this problem.
183
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Fig. 6. Lower basin calibration hydrographs.
4. SUMMARY AND CONCLUSIONS The comparative evaluation identified, from a review of 25 candidate hydrologic modeling systems, two models thought to be the most applicable to the alpine study site. These two models were the Streamflow Synthesis and Reservoir Regulation model (SSARR) and the Precipitation R u n o f f Modeling System (PRMS). These two models were developed and are maintained by the Corps of Engineers and the U.S. Geological Survey, respectively. Generally speaking, the models did equally well in terms of the two calibration measures used. However, other considerations led to the conclusion that PRMS was to be preferred for continuing work in alpine hydrology and for possible application to other similar situations. These considerations related to climatologic data requirements, ease of calibration and simulation of reservoir operations. The climatologic data requirements of PRMS are more modest than SSARR. At the minimum, PRMS requires only a daily record of precipitation, maximum temperature and minimum temperature. A daily record of solar radiation was used in the comparative application, b u t PRMS has the capability to generate this record internally from the other climate data and watershed characteristics. In addition to precipitation and maximum
184 temperature, SSARR required daily records of solar radiation, albedo, dewpoint temperature and wind. Since many, if not all, of these records would not normally be available at other similar sites, the model having the less restrictive data requirements is to be preferred. Calibration consists of the estimation and subsequent modification of parameter values describing the hydrologic characteristics of the watershed and controlling the corresponding model algorithms. Although SSARR has a somewhat smaller number of such parameters than PRMS, the estimation of their initial values was more difficult. This is because SSARR relies on a less physically-based lumped parameter approach to simulating hydrologic processes. As a result, initial parameter values (many of which take the form of tabular relations) involved more guesswork and a good deal of reliance on advice from program support personnel. In contrast, PRMS uses a more physically-based distributed parameter approach; many of the :initial parameter values could be estimated from observable catchment characteristics and a record of observed streamflow. In considering the transferability of the model to other similar situations, lesser reliance on outside user support is to be preferred. Because of its more physically-based structure, it was possible to develop an intuitive understanding of PRMS that was difficult to achieve with SSARR, which often seemed a "black box". This made modification of parameter values to achieve calibration more straightforward with PRMS. A semi-quantitative measure of this is reflected in the number of calibration runs made for each model. Nearly twice as many SSARR runs were required to achieve comparable levels of calibration {in terms of runoff volume and daily flow correlation). If time and cost were an object in applying the models to similar sites, the model requiring less of each is to be preferred. Lastly, consideration of simulated reservoir operations made PRMS more attractive than SSARR. It was very difficult to achieve smooth outflow hydrographs from the small reservoirs in the watershed using SSARR. While both models use fundamentally the same outflow routing approach, the specific algorithm structure in SSARR consistently produced extreme variations in outflow from day to day when reservoir levels were near spillway elevations. It was concluded that the small reservoirs of the study site were simply below the range of applicability of SSARR. In fairness, SSARR has been applied with considerable success to larger river and reservoir systems.
4.1. Continuing research efforts Work initiated in this study is continuing in several areas. Algorithms in PRMS have been modified to permit simulation of several modes of reservoir operations. These modifications will provide a tool for assessment of different operating strategies which may enhance system reliability. Work is also underway to develop a wind effects' component for use in PRMS. This
185 a l g o r i t h m will s i m u l a t e t h e e f f e c t s o f w i n d in r e l o c a t i n g snowfall within t h e w a t e r s h e d as well as p r e d i c t i n g m o i s t u r e losses d u e to s u b l i m a t i o n o f b l o w i n g snow. T h e w i n d e f f e c t s c o m p o n e n t will p r o v i d e a t o o l for assessing snowp a c k m a n a g e m e n t strategies such as d r i f t c o n t r o l using s n o w fences. S o m e p r e l i m i n a r y w o r k has also b e e n d o n e to d e v e l o p a f r o z e n soil c o m p o n e n t . T h e p r e s e n c e o f an i m p e r v i o u s l a y e r o f f r o z e n soil b e n e a t h t h e s n o w p a c k c o u l d b e e x p e c t e d t o significantly m o d i f y basin r u n o f f characteristics. T h e P R M S h y d r o l o g i c m o d e l also f o r m s a k e y e l e m e n t o f the L o n g T e r m Ecological R e s e a r c h ( L T E R ) p r o g r a m , a c o m p r e h e n s i v e s t u d y o f the e c o l o g y o f t h e alpine t u n d r a e n v i r o n m e n t , carried o u t b y t h e I n s t i t u t e o f Arctic and Alpine R e s e a r c h o f t h e University of C o l o r a d o .
ACKNOWLEDGEMENT F u n d i n g s u p p o r t f o r t h e w o r k d e s c r i b e d in this p a p e r was p r o v i d e d b y t h e C o l o r a d o C o m m i s s i o n o n Higher E d u c a t i o n and b y t h e N a t i o n a l Science F o u n d a t i o n ( D E B 8 0 1 2 0 9 5 ) .
REFERENCES Brendecke, C.M., Laiho, D.R., Holden, D.C., Charles, T.J. and James, E., 1984. Hydrologic water balance investigation of high altitude alpine watersheds of Colorado. Fin. Rep. to Colorado Comm. of Higher Education, Dep. Civ. Environ. Arct. Eng., Univ. of Colorado, Boulder, Colo. Burns, S., 1980. Alpine soil distribution and development, Indian Peaks, Colorado Front Range. Dissertation, University of Colorado, Boulder, Colo. (unpublished). Caine, N., Furbish, D.J. and Moses, T.A., 1983. Stream-flow data from the Green Lakes Valley 1981--1982. Univ. of Colorado, Inst. Arct. Alp. Res., Boulder, Colo., CULTER/ DR-83/2. Chow, V.T. (Editor), 1964. Handbook of Applied Hydrology. McGraw-Hill, New York, N.Y. Cundy, T.W. and Brooks, K.N., 1981. Calibrating and verifying the SSARR Model-Missouri River watersheds study. Water Resour. Bull, Am. Water Works Assoc., 17(10): 775--781. Furbish, D.J., Brendecke, C.M., Caine, N, and Moses, T.A., 1983. Ablation, evaporation and evapotranspiration in the Green Lakes Valley 1981--1982. Univ. of Colorado, Inst. Arct. Alp. Res., Boulder, Colo., CULTER/DR-83/3. Holden, D.C., 1983. A comparative study of two precipitation runoff models applied to a subalpine watershed. Master's Report, University of Colorado, Boulder, Colo. (unpublished). Laiho, D.R., 1983. A comparative evaluation of streamflow simulation models in a Colorado alpine environment. Thesis, University of Colorado, Boulder, Colo. (unpublished). Leavesley, G.H., 1973. A mountain watershed simulation model. Dissertation, State University, Fort Collins, Colo. (unpublished). Leavesley, G.H., Lichty, R.W., Troutman, B.M. and Saindon, L.G., 1982. Precipitation runoff modeling system: user's manual (draft). U.S. Geol. Surv., Denver, Colo., Water Resour. Invest. 82-XX.
186 Linsley, Jr., R.K., Kohler, M.A. and Paulhus, J.L.H., 1975. In: B.J. Clark and M.E. Margolies (Editors), Hydrology for Engineers. McGraw-Hill, New York, N.Y., 2nd ed. Liou, E.Y., 1970. OPSET: program for computerized selection of watershed parameter values for the Stanford Watershed Model. Univ. of Kentucky, Lexington, Ky., Kentucky Water Resour. Inst., Res. Rep. No. 34. Loslaben, M.V., 1983. Climatological data from Niwot Ridge, East Slope, Front Range, Colorado 1970--1982. Univ. of Colorado, Inst. Arct. Alp. Res., Boulder, Colo., CULTER/DR-83/10. Moreland, D.C., 1980. Soil survey of Larimer County area, Colorado. U.S. Dep. Agric., Washington, D.C. Retzer, J.L., 1962. Soil Survey Fraser Alpine Area Colorado. U.S. Government Printing Office, Washington D.C. U.S.A.C.E. (U.S. Army, Corps of Engineers), 1956. Snow hydrology. U.S. Army Corps Eng., Portland, Oreg. U.S.A.C.E. (U.S. Army, Corps of Engineers), 1960. Runoff from snowmelt. U.S. Army Corps Eng., Alexandria, Va., EM-110-2-1406. U.S.A.C.E. (U.S. Army, Corps of Engineers), 1975. Program description and user manual for SSARR model. U.S. Army Corps Eng., Portland, Oreg. U.S.D.A. (U.S. Department of Agriculture), 1972. Hydrology. In: National Engineering Handbook, Section 4. U.S. Dep. Agric., Beltsville, Md. U.S.D.A. (U.S. Department of Agriculture), 1975. Soil taxonomy: a basic system of soil classification for making and interpreting soil surveys. In: Agricultural Handbook No. 436. U.S. Government Printing Office, Washington D.C.
171
Elsevier Science Publishers B.V., A m s t e r d a m -- Printed in The Netherlands
[1] COMPARISON OF TWO DAILY STREAMFLOW SIMULATION MODELS OF AN ALPINE WATERSHED
C H A R L E S M. B R E N D E C K E 1 , D O U G L A S R. L A I H O 2 and D Y A N C. H O L D E N 1'*
1 Department o f Civil, Environmental and Architectural Engineering, University o f Colorado, Boulder, CO 80309 (U.S.A.) 2 Hydrologic Consulting Engineers Inc., Boulder, CO 80301 (U.S.A.) (Received J u n e 14, 1984; revised and accepted S e p t e m b e r 11, 1984)
ABSTRACT Brendecke, C.M., Laiho, D.R. and Holden, D.C., 1985. Comparison of t w o daily streamflow simulation models of an alpine watershed. J. Hydrol., 77: 171--186. A comparison of daily streamflow simulation by two different c o m p u t e r models has been c o m p l e t e d for an alpine basin in central Colorado, U.S.A. The c a t c h m e n t is part of a 33-km 2 area providing m o s t of the City of Boulder's raw water supply. It is located b e t w e e n 2964 and 4103 m a.s.1, in elevation along the Continental Divide 24 km west of Boulder, Colorado. The S S A R R m o d e l simulation, which makes generous use of lumpedt y p e parameters, was c o m p a r e d with the PRMS m o d e l simulation, which makes greater use of individually defined parameters, by examining their respective ability to fit observed hydrograph volumes and shape. Both c o m p u t e r models were found to simulate streamflow equally well. The PRMS m o d e l was f o u n d to be more suitable overall because of its ability to handle small c a t c h m e n t s and the generous use of individual physicallybased parameters.
1. I N T R O D U C T I O N
Water availability in the western U.S.A. is determined largely by snowmelt runoff from high-altitude source areas of major rivers, Despite the importance of alpine source areas to water supplies, knowledge of the hydrology of these high-altitude catchments is limited. Present methods of supply projection rely principally on snowpack observations at a relatively small number of sites and yield only seasonal volumetric estimates of water availability. The principal reason for the lack of knowledge of alpine hydrology is remoteness and the consequent difficulty of obtaining meteorologic and hydrologic data. As an example, less than a third of Colorado's stream gaging stations are located at elevations of more than ~ 2 5 0 0 m above * Present address: 235 W. Grove, Pomona, CA 91767, U.S.A.
172
mean sea level {a.s.1.) while the vast majority of surface water supplies in the State rely on runoff originating at higher elevations. Numerous water utilities (e.g., the City of Boulder) operate reservoir systems in alpine catchments. The growing population of Colorado and the rapidly increasing demand for water make sound management of these alpine watersheds imperative. It is particularly important to have an accurate prediction of the runoff rates in these catchments when drought conditions prevail at lower elevations, since the melting of snow and ice may represent virtually the only source of water available during these times.
1.1. Research approach This paper discusses the preliminary phase of an ongoing research program the aim of which is to better understand the hydrology of alpine watersheds (Brendecke et al., 1984). The results of this ongoing effort are expected to have practical application in the areas of water-supply system development and operation and in assessment of land-use management strategies. A particularly urgent aspect of the latter relates to wilderness access policies devised to cope with the rapidly increasing population along the Front Range urban corridor. The research discussed herein describes the screening and comparative evaluation of candidate hydrologic modeling systems upon which future work might build. The comparative evaluation was made by applying the t w o selected modeling systems to an alpine catchment.
1.2. Study site The study site consisted of a portion of the North Boulder Creek catchment c o m m o n l y known as the Green Lakes Valley. The study site, shown in Fig. 1, covers an area of ~ 7.6 km 2 ranging in elevation from 3274 to 4100 m (a.s.1.). The western boundary of the study site is the Continental D1
lu~ ~
,A CLIMATE STATIO~ ~-', SEGMENTBOUNDARY ~)
SEGMENT NUMBER
i
~RIDGE
N SCALE APPROX 1:32000
Fig. 1. Green Lakes Valley study site.
SADDLE
~
SILVER LAKE
173
Divide; drainage of the valley is to the east into North Boulder Creek and ultimately to the South Platte River. The Green Lakes Valley was divided into an upper and a lower basin. The upper basin is completely alpine in nature with steep rocky slopes, some permanent snow and ice fields, and low tundra vegetation on the valley floor. The upper basin contains two natural lakes, Green Lakes 4 and 5. The lower basin is more sub-alpine with somewhat flatter hillslopes and heavier vegetation extending to Douglas fir at the lower portions. The lower basin contains four lakes, Lake Albion and Green Lakes 1--3, all of which are artificial and are regulated for water-supply purposes by the City of Boulder. Streamflow-recording stations were established, as shown in Fig. 1, below Green Lake 4 and below Lake Albion. These stations provided a flow record against which the hydrologic models could be calibrated (Caine et al., 1983). Meteorologic data used in the models were obtained from nearby climate stations, also shown in Fig. 1, maintained by the Institute for Arctic and Alpine Research of the University of Colorado (Loslaben, 1983) and by the U.S. National Weather Service. Model calibration essentially consisted of adjustment of model parameters until stream flows predicted using meteorologic data matched the observed flow records.
2. M O D E L S C R E E N I N G A N D S E L E C T I O N
The first step in the research was a review of existing hydrologic modeling systems and programs, the aim of which was to identify those which were potentially applicable to the study site. The initial list of 25 modeling systems was screened to 8, then to 2 (Laiho, 1983). Desirable model characteristics used as screening criteria included the following: (1) use of a continuous daily water balance; ( 2 ) a distributed structure allowing separate representation of watershed segments; (3) reliance on physically-based process descriptions; and (4) use of an energy budget approach in determining snowmelt. The two selected models were the Streamflow Synthesis and Reservoir Regulation (SSARR) model developed by the North Pacific Division of the U.S. Army, Corps of Engineers, and the Precipitation R u n o f f Modeling System (PRMS) of the U.S. Geological Survey (U.S.G.S.). The following discussion describes briefly how each of these hydrologic models works. 2.1. SSARR model characteristics The SSARR model has been under development since 1956; the version used in this study was SSARR-4 which was completed in 1972 (U.S.A.C.E., 1975). A more recent version of SSARR is available, b u t at the time the research was begun, it was not in a form readily compatible with the Cyber ® 1 72 computer used in this study. SSARR has been applied extensively to
174
I
PRECIPITATION~
TEMPERATURE SNOW
ACCUMULATIOr
EVAPOTRANSPIRATION
MFLOW
Fig. 2. SSARR r u n o f f synthesis structure.
management of the Columbia River system and to other river systems in the U.S.A. and abroad. The SSARR model has three basic components: (1) a catchment and runoff synthesis component; (2) a river channel routing component; and (3) a reservoir regulation component. The runoff synthesis c o m p o n e n t was the primary object of interest in the comparative evaluation. A schematic showing the structure of the runoff synthesis c o m p o n e n t is shown in Fig. 2. The operating characteristics of the model may be briefly explained by following an element of precipitation input through the model until it becomes streamflow. The amount of precipitation falling on a portion of the
175
catchment may be adjusted to reflect differences in elevation, proximity to rainfall gages, and other factors. The form of precipitation (i.e., rain or snow) is determined from daily temperature data and will be discussed later. Net precipitation (which includes rainfall and snowmelt) is divided into runoff, soil-moisture increase and evapotranspiration loss by means of a user-supplied table of soil moisture index (SMI) vs. r u n o f f percent (ROP). The ROP is determined from the SMI at the end of the previous time step and total runoff then computed as ROP times net precipitation. The SMI is then updated by subtracting evapotranspiration and total runoff from the precipitation input and adding the remainder to soil moisture. R u n o f f is next divided into three parts: surface, subsurface and baseflow. Baseflow is computed from a user-supplied table of baseflow infiltration index (BII) vs. baseflow percent (BFP). In a manner similar to that used for total runoff, the BFP is determined from the BII of the previous time period and baseflow computed as BFP times total runoff. The BII is then updated for use in the next time period. The direct runoff which remains after baseflow has been subtracted from total runoff is next separated into surface and subsurface runoff. This is accomplished by reference to another user-supplied table relating surface runoff to direct runoff. This last relationship generally provides for increasing the relative volume to surface runoff in any time period as the volume of total direct runoff increases. The volume allocated to subsurface runoff cannot exceed a user specified maximum rate. The three components of runoff (i.e. surface, subsurface and baseflow) are each then routed through a series of hypothetical storage reservoirs designed to represent the time delay experienced by each on its way to the stream. These storage reservoirs are specified by the user as to number and time of storage in each. Water leaving the last storage reservoir of each component is summed to obtain the total contribution to streamflow for that time period. Several options exist in SSARR to model the accumulation and melting of snow. Those used in this study were the generalized snowmelt equation (GSE) with the snowband option (U.S.A.C.E., 1975). The GSE computes the saowmelt rate for the day as a function of short- and long-wave radiation, and convection--condensation from overlying air. Although based on an energy budget approach, the GSE does n o t provide continuous accounting of the heat content of the snowpack. The snowband option serves two functions in the computation of snowmelt runoff; the first is to determine the form of precipitation and the second is to determine the proportion of the snowpack subject to melting on a given day. Both of these determinations are based on air temperature and segment elevation. As mentioned previously, the SSARR model has two routing components, a channel routing component and a reservoir routing component. Due to the small size of the study site the time delay due to channel flow is negligible and was ignored. Time delay in lakes was dealt with using the reservoir routing c o m p o n e n t which relies on iterative solution of the storage equation.
176
2.2. PRMS characteristics The basic rainfall and snowmelt runoff features of PRMS were developed in 1972 for a small Rocky Mountain catchment (Leavesley, 1973). Since adoption by the U.S.G.S., numerous other features have been added. PRMS is now a fairly large modular system consisting of a main calling program (which controls time sequencing) and a library of subroutines which perform calculations representing various hydrologic and hydraulic processes. Usersupplied subroutines may be readily added to the system library. The version of PRMS used in this study was obtained from the U.S.G.S. in late 1982 (Leavesley et al., 1982).
I TEMPERATURE~
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~
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Fig. 3. PRMS runoff synthesis structure.
177
LI
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k I--
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SINK Fig. 4. Disposition of infiltrated water in PRMS.
The basic rainfall and snowmelt runoff features of PRMS used in the research are shown in Fig. 3. Using the modular structure of PRMS, components representing specific processes can be added or deleted. As before, the operating characteristics of the model may be understood by following an element of precipitation through it. Net precipitation (i.e. that remaining after interception) is first used to satisfy surface r u n o f f in PRMS. The depth of surface runoff is calculated using a contributing area concept. The proportion of the catchment segment which produces surface runoff (as if impervious) varies with the level of storage of water in a "recharge zone". The recharge zone comprises that upper soil layer from which direct evaporation can occur. The upper and lower limits of contributing area and the maximum storage capacity of the recharge zone are user-defined. What remains after subtraction of surface runoff is termed infiltration. The disposition of infiltrated water in PRMS is shown schematically in Fig. 4. Infiltration is first used to bring the upper soil zone (i.e. the soil layer from which evapotranspiration can occur} to field capacity, which is userdefined. Excess water is next used to satisfy a user-defined contribution to the groundwater reservoir and the remainder percolates to the subsurface reservoir. Water in the subsurface reservoir may become subsurface runoff or percolate to groundwater. The subsurface runoff volume and the percolation to groundwater are computed as functions of the a m o u n t of water in the subsurface reservoir. Moisture in the groundwater reservoir may be lost to a user-defined " s i n k " or routed, as a function of level of groundwater storage, to baseflow. Coefficients of all these routing functions are user supplied. The three components of runoff, surface, subsurface and baseflow, are then summed to obtain the total runoff from the catchment segment. Snow accumulation and melt are simulated in PRMS using a continuous water- and energy-balance approach; the water balance is computed daily
178 and the energy balance twice daily. Precipitation is classified as rainfall or snow on the basis of air temperature. The snowpack is represented in PRMS as a two-layer system. The surface layer is subject to heat transfer with the environment and with the underlying pack, while the underlying pack may exchange energy only with the surface layer. The surface layer melts only if there is a net heat transfer to it and if previous heat transfer has brought it up to freezing temperature. The net heat transfer TCAL is computed as: TCAL = CALN + CALD + CALP where CALN = night-period energy balance of long-wave exchanges between air, snow, and vegetation; CALD = day-period energy balance of long-wave exchanges plus short-wave radiation input; and CALP = energy content of precipitation, if any. Melt (which occurs in the surface layer) then adds heat to the underlying pack in the form of liquid water. Only when the underlying pack is brought up to freezing temperature and has reached its liquid water holding capacity does snowmelt reach the soil surface. Snowmelt is used first to satisfy the soft-moisture deficit and continues to infiltrate up to a user-defined maximum rate. Only if this rate is exceeded is surface runoff produced. Infiltrated water is treated in the model as discussed previously. There was no channel routing function in the version of PRMS used in this study. Flow was delayed in lakes by a hydrologic routing m e t h o d similar to that used in SSARR. Storage--outflow relations for each lake or reservoir were user-defined. Because the reservoir routing routines in PRMS were developed for natural lakes, some programming modifications were required to enable the model to simulate operation of the reservoirs in the lower basin.
3. COMPARATIVE APPLICATION Application of the two modeling systems to the study site consisted of four tasks: symbolic representation of the catchment, estimation of catchment parameters consistent with the symbolic representation, development of daily meteorologic data files for use as model input and model calibration. A special effort was made to use consistent symbolic representations and parameters wherever possible in both models to facilitate comparison of results.
3.1. Symbolic representation Both models allow the user to divide the catchment into segments which are hydrologically and/or climatologically similar. The same general scheme of catchment segmentation was used for both models and this scheme is shown in Fig. 1.
179
The upper basin of the study site was divided into four segments, based primarily on slope, aspect and water storage capacity (Laiho, 1983). Segment 1, at the head of the valley, is east-facing with little vegetation and several permanent snowfields. Segment 2 is predominantly north-facing with steep, rocky slopes and little vegetation. Segment 3 is predominantly southfacing with somewhat gentler slopes and more tundra vegetation. Segment 4 is the valley floor. It is relatively flat with a fairly well-developed soil profile, a great deal of tundra vegetation, and a significant amount of water storage capacity in the two lakes. The lower basin was divided into five segments, also based primarily on slope, aspect and water storage (Holden, 1983). Segment 5 consists of steep talus slopes with little vegetation and faces northeast. Segment 6 faces mostly south and has fairly steep slopes covered with talus and tundra vegetation. Segment 7 comprises most of the valley floor. It is fairly flat, contains three lakes, and is more heavily vegetated, with shrubs and krummholz. Segments 8 and 9, facing northeast and southwest, respectively, have the best developed vegetation, consisting of gentle slopes with stands of Douglas fir. In both models segments 1--4 were defined to be tributary to the flow recording gage below Green Lake 4, and segments 5--7 tributary to Lake Albion. Segments 8 and 9, along with water spilled or released from Lake Albion, were then made tributary to the flow-recording gage at Albion townsite. The hydrologic processes within each segment are simulated as previously described. 3.2. Estimation o f parameter values
While both models depict the same fundamental processes, differences in the specific relationships used lead to different parameter value requirements. As a result, the discussion below addresses the two models individually. Parameters used in SSARR can be viewed as falling into two general groups, those relating to processing of meteorologic data and those relating to generation of runoff from net precipitation at the ground surface. Initial value estimates for the meteorologic parameters were determined from three general sources of information: (1) the program user's manual and discussions with support personnel; (2) commonly available reference documents on hydrology and snow hydrology (e.g., U.S.A.C.E., 1956, 1960; Chow, 1964; Linsley et al., 1975); and (3)informal discussions with individuals familiar with the watershed combined with site visits and professional judgement. Section 4 of this paper discusses the relative ease or difficulty encountered in estimating values. Some values were subsequently modified during calibration. SSARR parameters relating to runoff generation are lumped, that is, a few gross relationships take the place of many detailed ones. The lumped
180
parameters essentially comprise the table of SMI vs. ROP and similar curves discussed earlier. The initial values for the lumped parameter-soil moisture relationships were determined from soil maps and surveys of the area (Moreland, 1960; Retzer, 1962; U.S.D.A., 1975; Burns, 1980). Hydrologic characteristics were determined primarily from Soil Conservation Service runoff curve numbers thought to most closely describe the watershed soil characteristics (U.S.D.A., 1972; Laiho, 1983). As with SSARR, parameter values required by PRMS may also be viewed as relating to meteorology and runoff generation. Initial value estimates for the meteorologic parameters were determined from the same three general sources of information used in SSARR. Some values for PRMS parameters relating to runoff generation could be determined from measurements and observations at the study site, e.g. recession coefficients, soilmoisture holding capacity, and depth of recharge zone.
3.3. Input data requirements Meteorologic input data required by the two models are listed in Table I. Unless otherwise noted, a daily record of each data type is required. Sources of these data were several climatologic stations operated in the catchment and on nearby Niwot Ridge and shown earlier in Fig. 1 (Furbish et al., 1983; Loslaben, 1983). Stations D1 and Saddle are maintained by the Institute of Arctic and Alpine Research of the University of Colorado and provided daily records of precipitation, temperature, humidity and solar radiation. The Silver Lake station within the watershed is operated by the National Weather Service and provided a daily precipitation record. In SSARR, meteorologic input to each catchment segment can be computed internally as a weighted average from many climate stations, the weights being user-defined. In PRMS, precipitation data from up to five climate stations may be used, provided that no more than one station is used per segment. For all other climate data, PRMS relies on use of a single TABLE I Meteorologic data requirements (daily values) SSARR
PRMS
Precipitation Temperature (max.) Solar radiation Basin albedo Dew point Wind velocity .2
Precipitation Temperature (max. and rain.) Solar radiation* 1
• 1 May be computed internally. • 2 Or constant average value.
181
station. For comparative purposes the assignment of climate data to catchment segments was the same in both models. 3.4. Calibration
Model calibration essentially involves modification of various parameter values until hydrologic performance predicted by the model more or less matches that observed in the catchment. Measures of hydrologic performance used in this process were runoff volume and hydrograph shape. The calibration process covered the water-year 1982 (Caine et al., 1983). Complete data for water-year 1983 were not available in time to permit discussion herein. Calibration of a complex hydrologic model is largely a trial-and-error process although some automated routines exist (Liou, 1970). It is generally accepted, however, that attention should first be given to parameters affecting runoff volume. Once observed and predicted volumes agree, parameters modifying hydrograph shape can be adjusted (Cundy and Brooks, 1981). Ideally, the calibration should cover several consecutive years to lessen impacts of climatologic anomalies (e.g., heavy late-season snowfall) within a particular year. Where this is not possible, a year close to average, hydrologically speaking, should be used. Based on a comparison of 1982 snowcourse data at the study site and averages from the preceding 15 years, water-year 1982 was only slightly below average (Laiho, 1983). Calibrations of the upper and lower basins of the Green Lakes Valley were done separately (Holden, 1983; Laiho, 1983). A numerical summary of the upper basin results is given in Table II and plots of the observed and predicted hydrographs are shown in Fig. 5a and b. Because the flowrecording station at Green Lake 4 is typically buried by snow and/or frozen from October to June, the runoff volume comparison was based on the period from July through September. As can be seen in the hydrograph T A B L E II Calibration summary Statistic
Streamflow volume as % of o b s e r v e d Daily s t r e a m flow c o r r e l a t i o n coefficient
u p p e r basin
lower basin
SSARR
PRMS
SSARR
PRMS
107 ( 1 1 3 )
91 ( 9 7 )
93
94
0.72 ( 0 . 8 5 )
0.71 ( 0 . 8 7 )
0.91
0.90
N u m b e r s in p a r e n t h e s e s reflect d e l e t i o n o f r u n o f f excess f r o m J u l y 1 9 8 2 s t o r m event.
182 D.4 ~N,3
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plots, a sizeable portion of the snowmelt runoff may have occurred before the recording gage began operating and the peak runoff may have been missed. This problem may be lessened in water-year 1983 as both flowrecording stations were at least intermittently operable before peak r u n o f f was reached. The sharp spike in observed runoff occurring at the end of July also deserves explanation. A period of three days of more or less continuous rainfall was under-recorded at climate station D1 leading to model underprediction of runoff. This discrepancy could be attributed to gage catch deficiency at D1, extremely localized patterns of precipitation missing DI, and/or to rapid rain-induced melting of isolated remaining snowfields (Laiho, 1983). To assess the effects of this discrepancy on model calibration, Table II also presents numerical summaries calculated neglecting the excess observed r u n o f f during those three days. Numerical summaries and hydrograph plots resulting from calibration of the lower basin are also given in Table II and are shown in Fig. 6a and b, respectively. It should be noted that streamflow entering the lower basin from Green Lake 4 was observed rather than predicted; that is, the models were modified to accept the observed flow record from Green Lake 4 as input. This was done in order to better assess differences in model performance between the essentially alpine upper basin and the more sub-alpine lower basin. The flow-recording gage at Albion town site was operational, at least intermittently, from late May through September, and the summaries in Table II are based on that total period with missing observations filled in by linear interpolation (Holden, 1983). This permitted model calibration to encompass a portion of the rising limb of the snowmelt hydrograph not possible in the upper basin. Also, because the climate stations used in the lower basin were different from the upper basin, the discrepancy due to the late July rainstorm is less evident. The rather extreme fluctuations in flow predicted by SSARR in July and August appear to be the result of the reservoir routing algorithm used in the model. Further calibration was ineffective in mitigating this problem.
183
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PRFIS
Fig. 6. Lower basin calibration hydrographs.
4. SUMMARY AND CONCLUSIONS The comparative evaluation identified, from a review of 25 candidate hydrologic modeling systems, two models thought to be the most applicable to the alpine study site. These two models were the Streamflow Synthesis and Reservoir Regulation model (SSARR) and the Precipitation R u n o f f Modeling System (PRMS). These two models were developed and are maintained by the Corps of Engineers and the U.S. Geological Survey, respectively. Generally speaking, the models did equally well in terms of the two calibration measures used. However, other considerations led to the conclusion that PRMS was to be preferred for continuing work in alpine hydrology and for possible application to other similar situations. These considerations related to climatologic data requirements, ease of calibration and simulation of reservoir operations. The climatologic data requirements of PRMS are more modest than SSARR. At the minimum, PRMS requires only a daily record of precipitation, maximum temperature and minimum temperature. A daily record of solar radiation was used in the comparative application, b u t PRMS has the capability to generate this record internally from the other climate data and watershed characteristics. In addition to precipitation and maximum
184 temperature, SSARR required daily records of solar radiation, albedo, dewpoint temperature and wind. Since many, if not all, of these records would not normally be available at other similar sites, the model having the less restrictive data requirements is to be preferred. Calibration consists of the estimation and subsequent modification of parameter values describing the hydrologic characteristics of the watershed and controlling the corresponding model algorithms. Although SSARR has a somewhat smaller number of such parameters than PRMS, the estimation of their initial values was more difficult. This is because SSARR relies on a less physically-based lumped parameter approach to simulating hydrologic processes. As a result, initial parameter values (many of which take the form of tabular relations) involved more guesswork and a good deal of reliance on advice from program support personnel. In contrast, PRMS uses a more physically-based distributed parameter approach; many of the :initial parameter values could be estimated from observable catchment characteristics and a record of observed streamflow. In considering the transferability of the model to other similar situations, lesser reliance on outside user support is to be preferred. Because of its more physically-based structure, it was possible to develop an intuitive understanding of PRMS that was difficult to achieve with SSARR, which often seemed a "black box". This made modification of parameter values to achieve calibration more straightforward with PRMS. A semi-quantitative measure of this is reflected in the number of calibration runs made for each model. Nearly twice as many SSARR runs were required to achieve comparable levels of calibration {in terms of runoff volume and daily flow correlation). If time and cost were an object in applying the models to similar sites, the model requiring less of each is to be preferred. Lastly, consideration of simulated reservoir operations made PRMS more attractive than SSARR. It was very difficult to achieve smooth outflow hydrographs from the small reservoirs in the watershed using SSARR. While both models use fundamentally the same outflow routing approach, the specific algorithm structure in SSARR consistently produced extreme variations in outflow from day to day when reservoir levels were near spillway elevations. It was concluded that the small reservoirs of the study site were simply below the range of applicability of SSARR. In fairness, SSARR has been applied with considerable success to larger river and reservoir systems.
4.1. Continuing research efforts Work initiated in this study is continuing in several areas. Algorithms in PRMS have been modified to permit simulation of several modes of reservoir operations. These modifications will provide a tool for assessment of different operating strategies which may enhance system reliability. Work is also underway to develop a wind effects' component for use in PRMS. This
185 a l g o r i t h m will s i m u l a t e t h e e f f e c t s o f w i n d in r e l o c a t i n g snowfall within t h e w a t e r s h e d as well as p r e d i c t i n g m o i s t u r e losses d u e to s u b l i m a t i o n o f b l o w i n g snow. T h e w i n d e f f e c t s c o m p o n e n t will p r o v i d e a t o o l for assessing snowp a c k m a n a g e m e n t strategies such as d r i f t c o n t r o l using s n o w fences. S o m e p r e l i m i n a r y w o r k has also b e e n d o n e to d e v e l o p a f r o z e n soil c o m p o n e n t . T h e p r e s e n c e o f an i m p e r v i o u s l a y e r o f f r o z e n soil b e n e a t h t h e s n o w p a c k c o u l d b e e x p e c t e d t o significantly m o d i f y basin r u n o f f characteristics. T h e P R M S h y d r o l o g i c m o d e l also f o r m s a k e y e l e m e n t o f the L o n g T e r m Ecological R e s e a r c h ( L T E R ) p r o g r a m , a c o m p r e h e n s i v e s t u d y o f the e c o l o g y o f t h e alpine t u n d r a e n v i r o n m e n t , carried o u t b y t h e I n s t i t u t e o f Arctic and Alpine R e s e a r c h o f t h e University of C o l o r a d o .
ACKNOWLEDGEMENT F u n d i n g s u p p o r t f o r t h e w o r k d e s c r i b e d in this p a p e r was p r o v i d e d b y t h e C o l o r a d o C o m m i s s i o n o n Higher E d u c a t i o n and b y t h e N a t i o n a l Science F o u n d a t i o n ( D E B 8 0 1 2 0 9 5 ) .
REFERENCES Brendecke, C.M., Laiho, D.R., Holden, D.C., Charles, T.J. and James, E., 1984. Hydrologic water balance investigation of high altitude alpine watersheds of Colorado. Fin. Rep. to Colorado Comm. of Higher Education, Dep. Civ. Environ. Arct. Eng., Univ. of Colorado, Boulder, Colo. Burns, S., 1980. Alpine soil distribution and development, Indian Peaks, Colorado Front Range. Dissertation, University of Colorado, Boulder, Colo. (unpublished). Caine, N., Furbish, D.J. and Moses, T.A., 1983. Stream-flow data from the Green Lakes Valley 1981--1982. Univ. of Colorado, Inst. Arct. Alp. Res., Boulder, Colo., CULTER/ DR-83/2. Chow, V.T. (Editor), 1964. Handbook of Applied Hydrology. McGraw-Hill, New York, N.Y. Cundy, T.W. and Brooks, K.N., 1981. Calibrating and verifying the SSARR Model-Missouri River watersheds study. Water Resour. Bull, Am. Water Works Assoc., 17(10): 775--781. Furbish, D.J., Brendecke, C.M., Caine, N, and Moses, T.A., 1983. Ablation, evaporation and evapotranspiration in the Green Lakes Valley 1981--1982. Univ. of Colorado, Inst. Arct. Alp. Res., Boulder, Colo., CULTER/DR-83/3. Holden, D.C., 1983. A comparative study of two precipitation runoff models applied to a subalpine watershed. Master's Report, University of Colorado, Boulder, Colo. (unpublished). Laiho, D.R., 1983. A comparative evaluation of streamflow simulation models in a Colorado alpine environment. Thesis, University of Colorado, Boulder, Colo. (unpublished). Leavesley, G.H., 1973. A mountain watershed simulation model. Dissertation, State University, Fort Collins, Colo. (unpublished). Leavesley, G.H., Lichty, R.W., Troutman, B.M. and Saindon, L.G., 1982. Precipitation runoff modeling system: user's manual (draft). U.S. Geol. Surv., Denver, Colo., Water Resour. Invest. 82-XX.
186 Linsley, Jr., R.K., Kohler, M.A. and Paulhus, J.L.H., 1975. In: B.J. Clark and M.E. Margolies (Editors), Hydrology for Engineers. McGraw-Hill, New York, N.Y., 2nd ed. Liou, E.Y., 1970. OPSET: program for computerized selection of watershed parameter values for the Stanford Watershed Model. Univ. of Kentucky, Lexington, Ky., Kentucky Water Resour. Inst., Res. Rep. No. 34. Loslaben, M.V., 1983. Climatological data from Niwot Ridge, East Slope, Front Range, Colorado 1970--1982. Univ. of Colorado, Inst. Arct. Alp. Res., Boulder, Colo., CULTER/DR-83/10. Moreland, D.C., 1980. Soil survey of Larimer County area, Colorado. U.S. Dep. Agric., Washington, D.C. Retzer, J.L., 1962. Soil Survey Fraser Alpine Area Colorado. U.S. Government Printing Office, Washington D.C. U.S.A.C.E. (U.S. Army, Corps of Engineers), 1956. Snow hydrology. U.S. Army Corps Eng., Portland, Oreg. U.S.A.C.E. (U.S. Army, Corps of Engineers), 1960. Runoff from snowmelt. U.S. Army Corps Eng., Alexandria, Va., EM-110-2-1406. U.S.A.C.E. (U.S. Army, Corps of Engineers), 1975. Program description and user manual for SSARR model. U.S. Army Corps Eng., Portland, Oreg. U.S.D.A. (U.S. Department of Agriculture), 1972. Hydrology. In: National Engineering Handbook, Section 4. U.S. Dep. Agric., Beltsville, Md. U.S.D.A. (U.S. Department of Agriculture), 1975. Soil taxonomy: a basic system of soil classification for making and interpreting soil surveys. In: Agricultural Handbook No. 436. U.S. Government Printing Office, Washington D.C.