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Development and Application

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Development and application of a spatially-distributedArctic hydrological and thermal process model(ARHYTHM)Ziya Zhang, Douglas L. Kane* and Larry D. HinzmanWater and Environmental Research Center, University of Alaska Fairbanks, Fairbanks, AK 99775, USAAbstract:A process-based, spatially distributed hydrological model was developed to quantitatively simulate the energyand mass transfer processes and their interactions within arctic regions (arctic hydrological and thermal model,ARHYTHM). The model ®rst determines the ¯ow direction in each element, the channel drainage network andthe drainage area based upon the digital elevation data. Then it simulates various physical processes: includingsnow ablation, subsurface ¯ow, overland ¯ow and channel ¯ow routing, soil thawing and evapotranspiration.The kinematic wave method is used for conducting overland ¯ow and channel ¯ow routing. The subsurface ¯owis simulated using the Darcian approach. The energy balance scheme was the primary approach used in energy-related process simulations (snowmelt and evapotranspiration), although there are options to model snowmeltby the degree-day method and evapotranspiration by the Priestley ±Taylor equation. This hydrological modelsimulates the dynamic interactions of each of these processes and can predict spatially distributed snowmelt,soil moisture and evapotranspiration over a watershed at each time step as well as discharge in any speci®edchannel(s). The model was applied to Imnavait watershed (about 2.2km2) and the Upper Kuparuk River basin(about 146 km2) in northern Alaska. Simulated results of spatially distributed soil moisture content, dischargeat gauging stations, snowpack ablations curves and other results yield reasonable agreement, both spatially andtemporally, with available data sets such as SAR imagery-generated soil moisture data and ®eld measurementsof snowpack ablation, and discharge data at selected points. The initial timing of simulated discharge does notcompare well with the measured data during snowmelt periods mainly because the eect of snow damming onruno was not considered in the model. Results from the application of this model demonstrate that spatiallydistributed models have the potential for improving our understanding of hydrology for certain settings.Finally, a critical component that led to the performance of this modelling is the coupling of the mass andenergy processes. Copyright#2000 John Wiley & Sons, Ltd.KEY WORDS spatially distributed hydrological model; arctic; soil moisture; snowmelt; evapotranspiration;stream¯ow; overland ¯ow; subsurface ¯ow; digital elevation model; permafrost; tundraINTRODUCTIONThe arctic ecosystem (de®ned here as an area of continuous permafrost) diers from those in more temperateregions, primarily because of the cold temperatures, dominance of snow cover and large annual variation insolar radiation. Past interests in hydrological processes in arctic regions were driven primarily by resourceCopyright#2000 John Wiley & Sons, Ltd. Received 8 September 1997Accepted 2 April 1999HYDROLOGICAL PROCESSESHydrol. Process. 14, 1017±1044 (2000)* Correspondence to: D. L. Kane, University of Alaska, Water and Environmental Research Center, Institute of Northern Engineering,460 Duckering Building, Fairbanks, AK 99775-5860, USA. E-mail: [email protected]/grant sponsors: Arctic System Science (ARCSS) Land Atmosphere Ice Interactions (LAII) Program, US National ScienceFoundation.Contract/grant numbers: OPP-9214927 and OPP-9318535.development; however, hydrological research is currently being con ducted for numerous other reasons. It isbroadly acknowledged that arctic regions play an important role in Earth's climate dynamics (Alley, 1995). Italso represents potentially important sources and/or sinks of greenhouse gases. A changing climate could inturn induce numerous biological and physical changes that could augment or retard global climate changeand signi®cantly impact arctic ecosystems (Rouse et al., 1997). Also, arctic environments are fragile systemsthat are relatively sensitive to anthropogenic impact and climate change (Roots, 1989; Kane et al., 1991b;Crawford, 1997; Rouse et al., 1997). Advancing our understanding of the hydrology of this region can beaccomplished by coupled ®eld and modelling studies. Reported here is the development and application ofan arctic hydrological and thermal model (ARHYTHM).Understanding coupled hydrological and thermal processes is essential when studying regional and globalclimatic change and its consequences. As a main linkage between atmospheric and terrestrial/aquaticsystems, hydrological processes are very important and yet complex. To quantify the interactive dynamics ofthe arctic system, a process-based and spatially distributed hydrological model that covers all of theimportant aspects of the water and energy balances and their interactions is needed. Such a process-based,spatially distributed hydrological model oers several advantages (Goodrich, 1990; Woolhiser et al., 1990;Wigmosta et al., 1994; Beven, 1996) over lumped conceptual or empirical models. First, it provides greateramounts of detailed information over the entire basin, rather than just lumped basin averages. Second, itsspatially distributed output data can be very useful when the model is coupled to other spatially distributedmodels such as those for chemical and biological processes.There are several hydrological models that have the ability to simulate spatial variation of physicalprocesses: such as the SHE model (SysteÂme Hydrologique EuropeÂen) (Jonch-Clausen, 1979; Abbott et al.,1986) and the TOPMODEL (Beven and Kirkby, 1979). Kite (1978, 1989) developed a simple lumpedreservoir parametric (SLURP) model to simulate hydrological responses of watersheds. Later, Kite andKouwen (1992) improved the same model by computing the rainfall±runo and snowmelt processesseparately for dierent land cover classes. By relating the model parameters to vegetation type, Kite (1993)used a similar model, SLURPÿGRU, to study the climate change and produce more realistic estimates ofthe resulting changes in stream¯ow. Kite et al. (1994) also combined a hydrological model with a GCM for amacroscale watershed. Wigmosta et al. (1994) presented a distributed hydrology±vegetation model thatincludes canopy interception, evaporation, transpiration, and snow accumulation and melting, as well asruno generation via the


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