Effects of land-cover changes on the hydrological response of interior Columbia River basin forested catchments

Transcription

1 HYDROLOGICAL PROCESSES Hydrol. Process. 16, (22) Published online in Wiley InterScience ( DOI: 1.12/hyp.117 Effects of land-cover changes on the hydrological response of interior Columbia River basin forested catchments James R. VanShaar, Ingjerd Haddeland and Dennis P. Lettenmaier* Department of Civil and Environmental Engineering, Box 3527, University of Washington, Seattle, WA 98195, USA Abstract: The topographically explicit distributed hydrology soil vegetation model (DHSVM) is used to simulate hydrological effects of changes in land cover for four catchments, ranging from 27 to 133 km 2, within the Columbia River basin. Surface fluxes (stream flow and evapotranspiration) and state variables (soil moisture and snow water equivalent) corresponding to historical (19) and current (199) vegetation are compared. In addition a sensitivity analysis, where the catchments are covered entirely by conifers at different maturity stages, was conducted. In general, lower leaf-area index (LAI) resulted in higher snow water equivalent, more stream flow and less evapotranspiration. Comparisons with the macroscale variable infiltration capacity (VIC) model, which parameterizes, rather than explicitly represents, topographic effects, show that runoff predicted by DHSVM is more sensitive to land-cover changes than is runoff predicted by VIC. This is explained by model differences in soil parameters and evapotranspiration calculations, and by the more explicit representation of saturation excess in DHSVM and its higher sensitivity to LAI changes in the calculation of evapotranspiration. Copyright 22 John Wiley & Sons, Ltd. KEY WORDS vegetation change; hydrological response; model comparison INTRODUCTION Changes in vegetation cover in forested watersheds affect the generation of runoff and stream flow in two primary ways. First, increased vegetation extent or leaf-area index (LAI) usually decreases runoff production, as a result of increased transpiration and/or evaporation of precipitation intercepted by the vegetation canopy (Waring and Running, 1998). Second, vegetation affects snow accumulation and ablation patterns. Usually, more vegetation (i) reduces the accumulated snow pack and (ii) reduces radiative heat transfers to the surface snowpack. The combined effect of these mechanisms is that the accumulated snowpack is often larger in a clearing than in an adjacent forested area (Storck, 2). On the other hand, during winter rain events ( rain-on-snow ) the typical pattern is that the ablation of snow in clearings is much greater than in forested areas owing to higher wind, hence enhanced turbulent heat transfer in the cleared areas (Storck, 2). These mechanisms lead to secondary hydrological changes. Reduced evapotranspiration results in increased soil moisture, which in turn increases the extent of saturation and hence can lead to more runoff during snow melt or heavy rainfall events. The relatively large snowpacks that accumulate in clearings may melt more rapidly than those under the forest canopy, owing to enhanced heat flux into the pack, and/or to greater incident solar radiation on the snow surface in spring. Various field experiments have examined the localized effects of land-cover change at the plot or subcatchment scales (see e.g. Rothacher, 1965, 197; Harr and McCorison, 1979; Ziemer, 1981; Megahan, 1983; Troendle and King, 1985; Berris and Harr, 1987; Kattelmann, 199). Although these investigations have helped in understanding the physical processes by which landscape disturbance affects local hydrological * Correspondence to: Dennis P. Lettenmaier, Department of Civil and Environmental Engineering, Box 3527, University of Washington, Seattle, WA 98195, USA. dennisl@u.washington.edu Current address: Riverside Technology Inc., 229 East Prospect Road, Suite 1, Fort Collins, CO 8525, USA. Received 18 March 21 Copyright 22 John Wiley & Sons, Ltd. Accepted 7 August 21

2 25 J. R. VANSHAAR, I. HADDELAND AND D. P. LETTENMAIER response, extrapolation to the catchment scale is complicated, because a catchment integrates a variety of landuse changes. There also is a question as to how changes at the plot scale are affected by their location within the catchment, which in turn partly determines the effect on catchment level hydrographs. The impossibility of isolating variables in the natural system limits the ability of field experiments to determine cause and effect (see Eberhardt and Thomas, 1991). If the so-called paired catchment approach is used to determine the magnitude of vegetation change effects on runoff, the catchments must be located near each other, and have divergent land-use histories. Furthermore, as vegetation is re-established following a disturbance (planned or otherwise), the magnitude of the differential effect is reduced, so that the largest changes tend to occur during the first few years of regrowth following the disturbance. There is, therefore, an element of chance in the occurrence of weather patterns that may or may not lead to altered response of treatment and control catchments during the period when land-cover differences are the greatest. Finally, the number of possible catchment pairs diminishes rapidly as catchment size increases, and interpretation of changes in large catchments is more confounded by cumulative effects. Alternatively, a hydrological model can be used to predict the effects of land-cover change on a catchment s hydrological response. Bowling et al. (2) used a variation of this approach by analysing residual values (observations model predictions) for trend in western Washington catchments that had undergone logging. The residuals method effectively pairs a catchment with itself; the observations reflect the effects of vegetation change, whereas the model simulations are run for a fixed vegetation condition. Because both the observed and simulated stream flows have been affected by the same sequence of weather and climate conditions, the differencing controls for these effects. Another model-based approach simulates scenarios, where different vegetation conditions can be used to explore the possible effects of vegetation change. This is the method used here. Matheussen et al. (2) investigated the effects of land-cover change on the hydrological response of the Columbia River basin using the macroscale variable infiltration capacity (VIC) hydrology model of Liang et al. (1994). The effects of historical land-cover change within the basin between 19 and 199 were investigated for nine large sub-basins of the Columbia, totalling 567 km 2. They found that the trend toward less mature vegetation within the Columbia River basin resulted in annual average increases of runoff ranging from 2Ð9 to 1Ð7% in the sub-basins, averaging to 1Ð2% near the outlet of the river. The increased runoff was balanced by decreases from.9 to 12Ð1% in annual average evapotranspiration. The Matheussen et al. (2) analysis was intended to identify patterns of change over the large sub-basins, typically with drainage areas of the order of 5 km 2. The coarse spatial scale of their study is insufficient to allow interpretation at the smaller catchment scale (typical drainage areas 1 1 km 2 ), where individual management decisions are made. The purpose of this investigation is to extend the Matheussen et al. (2) analysis from the sub-basin to the catchment scale, and to evaluate consistency or lack thereof of inferred catchment-scale changes with those inferred by Matheussen et al. at the much larger sub-basin scale. To address these issues, a highresolution spatially distributed hydrological model was used to simulate hydrological effects of vegetation changes in four catchments (drainage areas ranging from 27 to 133 km 2 ) within the Columbia River basin. The distributed hydrology soil-vegetation model (DHSVM) of Wigmosta et al. (1994) was the basis for this study. The DHSVM represents interactions between vegetation and catchment hydrological processes in a way that facilitates extension of the Matheussen et al. (2) approach to finer spatial scales. Previous applications of DHSVM have investigated, for instance, the effects of logging and forest roads on floods (Bowling and Lettenmaier, 21; La Marche and Lettenmaier, 21). BACKGROUND The development of the Columbia River basin s resources has greatly changed the face of the landscape over the past 15 years. Within the USA portion of the basin, only 3% of the current landscape is estimated to have characteristics similar to historical conditions (USDA, 1996). Federal and State irrigation projects

3 LAND COVER CHANGE EFFECTS 251 have replaced deserts within the basin, and farming and ranches have converted shrub land to dry farms, and cattle have taxed the vegetative capacity of rangeland. Even more important from a hydrological standpoint, forestry practices have changed the face of the land by interfering with the natural successional sequence. Fire suppression and certain management practices have worked to increase density or forest extent on the one hand, whereas selective and clearcut harvesting and stand replacing fires have reduced the average forest maturity on the other. Furthermore, although not the focus of this paper, construction of reservoirs has greatly altered the shape of runoff hydrographs on the main stem of the Columbia and many of its major tributaries. The total water storage capacity in the reservoirs within the Columbia River basin is 67 8 Mm 3, which is about 3% of an average year s runoff at The Dalles, Oregon (Bonneville Power Administration, 21). Within the Columbia River basin, the forested uplands receive the greatest precipitation and thus dominate the hydrology of the system (Matheussen et al., 2). The hydrological importance of the forested uplands is enhanced by generally lower evaporative demands than are experienced in the lowlands. According to the U.S. Forest Service, 66% of the forested area within the USA portion of the Columbia River basin is managed for harvest. Over this area, forest maturity generally has been reduced relative to the historical condition (USDA, 1996), and it is this signal of change that probably is the primary determinant of the Matheussen et al. (2) assessment of increased runoff. Of the remaining 34%, most is in reserve (or wilderness) areas, where limited fire suppression occurs. MODEL DESCRIPTIONS The DHSVM (Wigmosta et al., 1994; Wigmosta and Lettenmaier, 1999) is a physically based spatially distributed hydrological model that describes the effects of soil, vegetation and topography on the movement of water at and near the land surface. The model forcings are time-series of meteorological variables, primarily precipitation, temperature, solar and long-wave radiation, relative humidity and wind. The DHSVM solves the water and energy balance at each pixel for multiple soil and vegetation layers. The model tracks the fate of precipitation as direct runoff, infiltration and subsequent subsurface storage and contribution to runoff or extraction as evapotranspiration. Subsurface moisture is redistributed laterally in the saturated zone using a downslope/down-gradient algorithm. A vegetation canopy submodel represents the attenuation of wind and solar radiation and interception, storage, and conditions and fate of both solid and liquid incident precipitation. A road and channel network (Wigmosta and Perkins, 21) is imposed on the catchment description, and this allows for interception of subsurface moisture by a channel as the water table intercepts the channel cut, and subsequent routing of water to the catchment outlet using a linear routing scheme. The DHSVM usually is implemented at spatial resolutions of 3 to 2 m. The VIC model (Liang et al., 1994) is a macroscale model that solves the full water and energy balances. The soil column is partitioned into a variable number of soil layers. Infiltration into the top soil layer is controlled through a variable infiltration curve, and release of baseflow from the pixel is controlled through a baseflow curve. The VIC model usually is implemented at spatial resolutions much coarser than those used by DHSVM, typically 1/8 to 2 latitude by longitude. Runoff produced at a VIC grid cell is routed to the grid-box outlet, and transported to the basin outlet using a river routing scheme. However, the catchments modelled in this study were small (comprising 2 16 VIC grid cells). For this reason, the VIC river routing scheme was not activated. MODEL IMPLEMENTATION The application of VIC to the Columbia River basin at 1/4 resolution, and the development of the historical and current land-cover vegetation are described in Matheussen et al. (2). Subsequently, the model was implemented at the higher 1/8 resolution to be compatible with the resolution used by the Land Data

4 252 J. R. VANSHAAR, I. HADDELAND AND D. P. LETTENMAIER Assimilation System (Mitchell et al., 1999) in which VIC is being used to simulate the surface hydrology of the entire USA. The 1/8 implementation for the Columbia basin is described briefly in Miles et al. (2). Study catchments Four catchments distributed throughout the USA portion of the Columbia River basin were the focus of this study (see Figure 1). Topographical and hydrometeorological data used for the implementation of DHSVM for each catchment are listed in Table I. Mica Creek is a tributary to the St Joe River in Northern Idaho, and drains forested mountainous terrain. The catchment has a history of fires and logging, but today most of the basin is covered with mature second growth forest. The Swan River drains into Swan Lake and eventually into the Flathead Lake in Western Montana. Large fires in the early part of the twentieth century, in addition to timber production, has resulted in areas of harvest and various stages of regrowth interspersed through the lower portion of the basin. The Entiat River drains the east side of the Cascade Mountains directly into the Columbia River, north of Wenatchee, Washington. During the turn of the previous century, the Entiat River was heavily grazed and logged. Logging continues in the middle and lower part of the catchment, although much of the logging activity is salvage of burned areas. Mores Creek, the largest of the basins studied, drains a portion of the Boise Mountains north-east of Boise, Idaho. Gold was discovered in the catchment in 1862, and most of the lower valleys have been disturbed through placer mining operations and logging. For the past 5 years, fire suppression and logging limitations have resulted in increased forest maturity. Entiat Mica Swan Canada USA Columbia River Basin Pacific Ocean Mores km 5 Figure 1. Location of the study areas. Table I. Summary statistics for study catchments Area Elevation range Mean annual Mean annual runoff b (km 2 ) (m) precipitation a (mm) (m 3 /s) (mm) Mores Creek Ð Entiat River Ð5 Swan River Ð5 Mica Creek 26Ð Ð45 a Based on PRISM precipitation fields (Daly et al., 1994). b Based on 2 years of observations (for Mica, only 7 years of data were available).

5 LAND COVER CHANGE EFFECTS 253 Surface characteristics data Elevation data were derived from 3-m digital elevation models (DEM) obtained from the United States Geological Survey (USGS) Mapping Program (USGS, 1993) and the National Elevation Database (USGS, 1999). The DEMs were aggregated so that each of the catchments could be represented with roughly 1 pixels or less; a number selected primarily on the basis of computational constraints. This resulted in a pixel size of 12 m for Mores, 9 m for Entiat, 6 m for Swan and 3 m for Mica. Current vegetation data were extracted from classified satellite imagery (Landsat Thematic Mapper at approximately 3 m resolution), U.S. Forest Service records, and proprietary records of landowners. In addition to land-cover classification, these sources provided additional information such as crown closure and vegetation height. Vegetation and land cover were categorized into one of the 3 basic land-cover types used in Matheussen et al. (2), who in turn based these cover types and parameters (i.e. LAI, height, stomatal resistance) on data produced by Thornton and White (1996) as part of the federal Interior Columbia Basin Ecosystem Management Project (ICBEMP) (Quigley and Arbelide, 1997). In addition, the land-cover types were subdivided according to fractional cover. Current and 19 vegetation-cover classifications at 1 km resolution, produced by ICBEMP, were available for all four catchments. The 19 vegetation cover is a compilation of archived vegetation maps and government records, whereas the current vegetation cover was estimated from AVHRR satellite imagery (Thornton and White, 1996; Quigley and Arbelide, 1997). The vegetation images at 1 km resolution are the same as those used by Matheussen et al. (2). For Mica Creek, a second current vegetation-cover data set was provided by the Potlatch Corporation, based on their classification of Landsat imagery using ground-truth data within the catchment. In addition, Potlatch provided 1933 aerial photos for Mica Creek from which the extent of clearcut and burned areas were identified. The primary source of soils data was the United States Department of Agriculture National Resource Conservation Service s (NRCS) State Soil Geographic (STATSGO) database (USDA, 1994). Information about forest road locations was provided by U.S. Forest Service Districts and by Potlatch Corporation and Boise Cascade Corporation in the case of Mica Creek and Mores Creek, respectively. Culvert locations (required by the DHSVM road algorithm) were mapped in the field or inferred from road location and topographic data using methods described by La Marche and Lettenmaier (21). The LaMarche and Lettenmaier approach calculates culvert densities for each catchment based on limited field observations, and then uses the estimated culvert density to infer culvert locations according to mapped road characteristics over larger areas where direct observations of culvert locations are not available. Climatological and hydrological data Climatalogical and stream discharge data were extracted from the National Climatic Data Center (NCDC) and USGS, respectively. In addition, precipitation and temperature data were acquired for some NRCS SNOTEL gauges. Precipitation and stream-flow data were available at daily time-steps, and temperature data were available as daily maximum and minimum temperatures. Hourly temperatures were estimated through interpolation using Hermite polynomials as described in Conte and de Boor (198). Daily precipitation was disaggregated to hourly time-steps by applying an hourly to daily depth ratio calculated at nearby stations with hourly observations. Wind data were obtained from the NCEP/NCAR reanalysis (Kalnay et al., 1996). For Mores Creek, relative humidity was taken from a nearby airport (Boise), otherwise it was estimated from daily minimum temperature using the method of Kimball et al. (1997). Long-wave radiation was estimated using the method outlined in Bras (199), whereas short-wave radiation at the gauges was calculated based on the solar calendar and an estimate of atmospheric transmittance (Tennessee Valley Authority, 1972; Gates, 198; Curtis and Eagleson, 1982; Bras, 199). Parameter estimation For each catchment, DHSVM parameters were estimated using prior information (e.g. soils data), which subsequently were adjusted via trial and error to obtain agreement of observed and predicted stream flow

6 254 J. R. VANSHAAR, I. HADDELAND AND D. P. LETTENMAIER at the catchment outlets. Initial conditions for soil moisture, interception storage, snow water equivalent and saturation extent were obtained by running the model for 1 year (October through to September) prior to the period used for parameter estimation. Parameters were estimated on the basis of agreement between predicted and observed stream flow using 4 years of data, and subsequently were verified using the following 4 years of observations. The specific time periods used differ somewhat from catchment to catchment, owing to data availability. For Mica Creek the combined period for parameter estimation and model evaluation was 1 year shorter (7 years) than for the other three catchments. For Mores Creek, Entiat River and Mica Creek monthly precipitation climatologies produced using the precipitation regression on independent slopes method (PRISM) of Daly et al. (1994) were used to distribute the precipitation spatially. The simulated annual precipitation does not match PRISM values at all catchments, as a result of differences in measured precipitation values and the corresponding PRISM value. In the Swan River basin, use of PRISM precipitation fields resulted in unrealistically large snowpacks in the upper parts of the catchment, which generally melted rapidly late in the season causing substantial overestimates of stream flow. For this reason, we opted to base Swan River precipitation on variable lapse rates, calculated using nearby gauge data. Time-varying temperature lapse rates were calculated, and initially implemented, in Mores Creek, Entiat River and Swan River. However, for all catchments other than the Swan River, better simulation results were obtained by implementing spatially distributed temperature using a constant lapse rate of Ð6 C per 1 m (approximately the psuedo-adiabatic lapse rate). Vegetation parameters and root depths were associated with the vegetation classifications, and were not changed for individual catchments. On the other hand, soil parameters were adjusted in the parameter estimation stage, so the relationships between the model soil parameters and the STATSGO soil data vary from catchment to catchment. Initially, there was a tendency for the simulated stream flow to overestimate the observations, especially in the spring and early summer. This implies (apart from the possibility of misestimation of precipitation) that the model was underestimating evapotranspiration. This bias was mostly removed by increasing soil porosity (Entiat and Mores catchments), which allowed for more storage of water in the summer, which in turn leads to more evapotranspiration. Because most of the north-west of USA experiences winter-dominant precipitation, much of which is stored in the winter snowpack, soil moisture is more available to vegetation in the spring and early summer than later in the summer. For this reason transpiration tends to peak during this period, even though the seasonal peak of net radiation occurs later. Other than Mica Creek, the lower portions of the catchments are dominated by brush. To account for the increased transpiration in the spring and early summer, the leaf-area index (LAI) of the mountain brush was increased from 1Ð5 to2ð5 in the period from April through to June. This increase in LAI, as well as the increase in soil porosity for two of the basins, resulted in a better agreement between observed and simulated stream flow. Figure 2 shows the resulting simulated and recorded stream flow for all catchments. Spring stream flow is still slightly overestimated, but overall recorded and observed streamflow are in acceptable agreement for all catchments, both with respect to timing and discharge volume. For the Entiat River, a large flood event in November 199 is overpredicted both in terms of peak and volume discharge (simulated maximum discharge is 22 m 3 /s). The meteorological forcing data at the Pope Ridge SNOTEL and two nearby meteorological stations are in approximate agreement on the precipitation volumes during this event, which suggests that the catchment did in fact experience heavy precipitation at that time. Review of the observed snow water equivalent during this period shows that part of the catchment was accumulating snow while other parts were experiencing snowmelt. Figure 3, which compares the simulated and observed snow water equivalent at the Pope Ridge SNOTEL shows that some snow was accumulating in November 199. The contrast between the modelled rapid melt, and observed upper elevation snow accumulation, suggests that the temperature lapse rate used in the simulations was not able to distribute rain and snow in the catchment accurately during, or previous to, this event. Generally, though, the observed and simulated patterns of snow accumulation and melt are in general agreement (see Figure 3). At the Skylark Trail SNOTEL (in the Swan River catchment), the simulated snow water equivalent is somewhat less than observed. The underestimation is largest in 1988, when maximum

7 LAND COVER CHANGE EFFECTS 255 Streamflow (m 3 /s) Mores Simulated Recorded Streamflow (m 3 /s) Entiat Simulated Recorded Streamflow (m 3 /s) Swan Simulated Recorded Streamflow (m 3 /s) Mica Simulated Recorded Figure 2. Simulated and recorded stream flow at Mores Creek, Entiat River, Swan River and Mica Creek simulated snow water equivalent is only about 63% of observed. However, observed and simulated stream flow are in good agreement that year (Figure 2), and the recorded discharge volume suggests that the accumulated snowpack should not have been much above normal that year. The large difference during that winter probably is attributable to lower lapse rates for several key storms, which resulted in misclassification of the form of precipitation. The temperature lapse rate for the Swan River was calculated based on temperature data from a meteorological station within the Swan River and a SNOTEL site (North Fork Jocko) south-west of the

8 256 J. R. VANSHAAR, I. HADDELAND AND D. P. LETTENMAIER Snow water equivalent (mm) Entiat Simulated Recorded Snow water equivalent (mm) Swan Simulated Recorded Snow water equivalent (mm) Mica Simulated Recorded Figure 3. Simulated and recorded snow water equivalent at Pope Ridge SNOTEL (Entiat River), Skylark Trail SNOTEL (Swan River, discontinued in early 199) and Mica Creek SNOTEL catchment. Unfortunately, temperature observations are not available for the Skylark Trail SNOTEL station, so verification of the simulated temperatures is not possible. For Mica Creek, the model was also run using the Potlatch vegetation cover data. Because the vegetation parameters differ from those used for the other simulations, other model parameters had to be adjusted somewhat to obtain reasonable simulations of observed stream flow. Once this was accomplished, results similar to those shown in Figure 2 were achieved. Vegetation scenarios To study the changes in stream flow associated with land-cover changes, two vegetation scenarios were used; representing the current (c. 199) and historical (c. 19) conditions. Because no historical fine-resolution vegetation images existed, they had to be inferred from the current and historical images at 1 km resolution, and the current fine-resolution image. The constructed fine-resolution historical vegetation images were based on two assumptions: 1. The current fine-resolution image represents the current vegetation coverage correctly, both in the sense of vegetation types and their distribution;

9 LAND COVER CHANGE EFFECTS The difference between the two coarse resolution vegetation images describes the change in the vegetation s age and LAI within each 1 km (coarse resolution) grid cell. The historical fine-resolution vegetation image was constructed by changing the age of the vegetation in the fine resolution current vegetation image according to the age difference between the two coarse-resolution vegetation images. If the historical and current coarse-resolution vegetation images indicated that the dominant vegetation type within a grid cell had changed, the fine-resolution vegetation image was changed accordingly. In the latter case, vegetation within the 1 km 2 area was distributed based on the vegetation s probability of existing on certain aspects and slopes. This probability was extracted from the current fine-resolution vegetation image. Vegetation types that did not appear in the current fine resolution vegetation image were assigned probabilities based on existing information about similar vegetation types. Barren areas at high elevations and water were kept at current locations. In the resulting historical fine-resolution image, vegetation of the same type (e.g. conifers) was allowed to mix along the grid borders. In addition, deciduous trees that appeared only in the historical vegetation image were assumed to occur mainly on lower elevations not too far from their original location in the historical coarse-resolution image. The Mica Creek 1933 vegetation image was based on aerial photographs obtained from Potlatch Corporation. The aerial photographs were used primarily to identify areas of recent harvest and/or fire, which were assigned to the lowest maturity class. Otherwise, vegetation types were assumed to be similar to those in the Potlatch 199 image. The current coarse-resolution vegetation image for Mica Creek classifies more than 75% of the vegetation as deciduous. However, both the current fine-resolution vegetation images (TM and Potlatch), as well as our site visit, show almost exclusively conifers in this area. Hence, the areas classified as deciduous in the coarse-resolution image were changed to conifers of the same maturity. To analyse the hydrological effects of vegetation cover at different stages, a sensitivity test was performed in which the models were run for a situation where each catchment was covered by only one vegetation type (conifer, middle drought tolerant) at an early stage and at a middle stage. The intent of these simulations was to study the isolated effects of LAI changes within and between the models. For these scenarios, a fractional vegetation cover of Ð85 was assumed. Areas at high elevations, which today are barren, were kept barren. In low-precipitation areas, the fractional cover was set to Ð5. The precipitation threshold was estimated from figures for Douglas-fir (pseudotsuga) provided by Thompson et al. (1999). MODEL-PREDICTED HYDROLOGICAL EFFECTS OF HISTORICAL LAND-COVER CHANGES General results Figure 4 shows the vegetation cover in all four catchments for historical and current conditions. The figure shows a general trend towards younger vegetation for current as compared with historical conditions, which is a result of logging, fires and grazing. It also indicates that many of the deciduous trees that apparently existed in these catchments at the onset of the twentieth century, had disappeared 9 years later. The changes in land-cover can be summarized as changes in LAI over each of the catchments, which are listed in Table II. The relatively large difference in LAI between the two current vegetation images (coarse and fine) mainly results because few pixels in the coarse-resolution image are classified as brush or barren (meaning low LAI). Also, fractional coverage of overstory in the coarse-resolution images is taken as 1, whereas the average fractional coverage is less in the fine-resolution images. The DHSVM includes a representation of the effects of forest roads on runoff, both through their effect on the channel drainage network, and the capacity of road cuts to intercept the water table and divert subsurface flow to streams (Wigmosta and Perkins, 21; LaMarche and Lettenmaier, 21). During the parameter estimation stage, the road algorithm was implemented. However, for the historical (19) condition, few roads were present. Roads are a source of hydrological change in a catchment, and since the purpose of this paper is to investigate the hydrological effects of vegetation changes, roads were excluded in the simulations

10 258 J. R. VANSHAAR, I. HADDELAND AND D. P. LETTENMAIER Figure 4. Historical (c. 19) and current (c. 199) vegetation cover

11 LAND COVER CHANGE EFFECTS 259 Table II. Average LAI values Fine-resolution images Current Historical Relative change current/historical Coarse-resolution images Current Historical Relative change current/historical Mores 3Ð79 3Ð39 1Ð12 7Ð66 6Ð46 1Ð19 Entiat 4Ð88 5Ð19 Ð94 7Ð8 7Ð38 Ð95 Swan 4Ð49 4Ð41 1Ð2 7Ð92 7Ð73 Ð98 Mica 9Ð48 7Ð91 1Ð2 1Ð5 8Ð6 1Ð23 Mica-Potlatch 1Ð36 3Ð36 3Ð8 used for the comparison studies. Furthermore, roads are not included in the VIC model, and excluding the roads therefore reduces the model dissimilarities in the model comparison analyses. Table III summarizes the mean annual water balance for each study area, for current and historical conditions. The model simulations in general show the highest runoff ratios occur where the LAIs are lowest. This is consistent with an expected increase in evapotranspiration with increased leaf area. Also, higher LAI usually results in higher snow vapour fluxes (sublimation), which occurs primarily from intercepted snow. Owing to higher wind in the canopy, relative sublimation of intercepted snow can be considerably higher than of snow on the ground (Storck, 2). However, it should be noted that the mean annual snow vapour flux, even for the high LAI cases, is always less than 6% of annual precipitation. Figure 5 shows the spatially distributed snow water equivalent on 1 March (mean values over the simulation period), and the mean snow water equivalent on 1 May, when basin average snow water equivalent is decreasing. The figure also shows the changes in winter LAI between current and historical vegetation conditions. On 1 March, the changes in snow water equivalent at higher elevations, where temperatures are low and winter conditions stable, generally reflect the changes in LAI. On 1 May, and on 1 March at lower elevations, the pattern is more complicated, because of interactive effects of temperature and radiative fluxes. When forest density decreases, either because of decreased fractional coverage or decreased LAI, more snow accumulates on the ground, but it also melts more rapidly because of enhanced energy transfer (heat and radiative fluxes) during snowmelt. The spatial patterns of evapotranspiration change were investigated at two times during the summer season; on 1 June, when the soils are still moist because of snowmelt, and on 1 September, when soil moisture is much lower. The current evapotranspiration values (mean over the simulation period), in addition to the residual Table III. Simulated mean annual precipitation (P), stream flow (Q), evapotranspiration (ET) and snow vapour flux (S) Water P Q ETC S 1Q 1 ETC S Q ET C S years (mm) current current 1 current current current/ current/ historical historical Storage historical historical historical historical (mm) (mm) (mm) (mm) (mm) Mores C C C16 Ð94 1Ð4 Entiat C 4 C C 51 C3 C Ð7 Ð9 Swan C C C8 8 1Ð1 Ð99 Mica C C C26 Ð96 1Ð4 Mica Potlatch C C C367 Ð63 1Ð86

12 251 J. R. VANSHAAR, I. HADDELAND AND D. P. LETTENMAIER Figure 5. Spatially distributed snow water equivalent on 1 March and 1 May for current conditions, and difference in snow water equivalent using current and historical vegetation data

13 LAND COVER CHANGE EFFECTS 2511 Figure 6. Spatially distributed evapotranspiration on 1 June and 1 September for current conditions, and difference in evapotranspiration using current and historical vegetation data

14 2512 J. R. VANSHAAR, I. HADDELAND AND D. P. LETTENMAIER values (current historical) are shown in Figure 6. Figure 6 also shows the changes in LAI for summer. The spatial changes in evapotranspiration in general reflect the spatial changes in LAI. However, increased LAI does not lead directly to increased evapotranspiration. Changes in snowmelt might contribute to reduced evapotranspiration in some areas that currently have higher LAI values than historically. Evapotranspiration is also dependent on stomatal resistance, which is represented in the DHSVM evapotranspiration algorithm, which in turn is based on the Penman Monteith method (Shuttleworth, 1992). In the vegetation parameters used in this study, stomatal resistance is lower for deciduous trees than for conifers, which might lead to increased evapotranspiration despite a decrease in LAI. Furthermore, seasonal changes in evaporative demand can influence the relative changes based on annual averages. For instance, increased evapotranspiration early in the summer might lead to decreased soil moisture and hence decreased evapotranspiration later in the season. Two to five daily peak stream-flow values were selected from each simulation year for evaluation. Figure 7 shows current stream-flow values divided by the corresponding historical stream-flow values for these cases. The predicted peak flows generally are highest when basin averaged LAI is lowest, but the figure also shows that the individual peak values do not always follow the general pattern. Changes in LAI also lead to changes in the timing and amount of snow accumulation and snow melt, which can result in changes in soil moisture, and hence peak flows, over the year. The LAI changes can result from changes in vegetation maturity, where an increase in LAI normally results in increased snow vapour flux, decreased undercanopy snow water equivalent, and increased evapotranspiration. However, if the LAI changes result from a shift in the relative characteristics (and presence or absence) of overstory and understory, the hydrological response might differ from this general pattern. Also, the climatic conditions under which the LAI changes occur will impact the hydrological response to LAI changes, and in particular seasonal changes in snow water equivalent, runoff and evapotranspiration. Although changes in average LAI are a reasonable indicator of overall catchment response, the spatial pattern of LAI changes within a catchment, and the interactions with temperature and precipitation amount and form throughout the catchment, will lead to spatial differences in the hydrological response. Hence, subcatchment responses result 1.4 Mores 1.4 Entiat Current/Historical Swan 1.4 Mica Current/Historical m 3 /s m 3 /s Figure 7. Current stream-flow values divided by the corresponding historical stream-flow values for two to five daily peak values for each simulation year

15 LAND COVER CHANGE EFFECTS 2513 in catchment average responses that may, or may not, follow the general pattern. Some catchment-specific examples of how hydrological response is influenced by interactions between temperature, precipitation and spatial distribution of vegetation changes are discussed in the following subsection. Catchment examples Even though the current vegetation in Mores Creek is younger than the historical (c. 19) vegetation, the LAI has increased. This is caused by the fact that in the historical conditions a large fraction of the basin (67%) was covered by late stage, highly moisture-tolerant conifers, which (according to the vegetation parameters) have a higher LAI in the middle stage than in the late stage. Maximum snow water equivalent in Mores Creek is highest for current conditions, even though basin-averaged change in winter LAI is CÐ65 m/m. However, the current LAI (overstory only) at elevations higher than 2 m is Ð35 m/m less than the historical LAI. During the snow accumulation period (here taken as December through to February) the precipitation in this high-elevation area is 1Ð6 times that below 2 m. However, this alone is not enough to explain why the maximum snow water equivalent is highest for current conditions (the area above 2 m is less than 1% of the total area in the Mores Creek Basin). Maximum snow water equivalent in this catchment occurs around 1 March. At this time, snow is accumulating at higher elevations, but melting has already started at lower elevations, causing current snow water equivalent to be lower than historical snow water equivalent in some areas where current LAI is higher than historical LAI. On 1 February, the contribution to basin-averaged mean difference in snow water equivalent (current historical) in areas where the change in LAI is more than C1 m/m is Ð16 mm, whereas it is C1Ð28 mm where the change in LAI is less than 1 m/m. A month later, on 1 March, the differences are CÐ25 mm and C1Ð75 mm, respectively. For the Entiat catchment, the mean (over 7 8 years) maximum snow water equivalent is highest for lower LAI (current conditions), which would be expected. However, despite more snow on the ground, some of the peak stream-flow values are lower for lower LAI. At current conditions, snow melts at about the same time as when LAI is higher. The lower peak stream-flow values for current vegetation all occur during the winter or early spring. This catchment is, on average, wettest under current conditions, but increased snowmelt at lower elevations in the winter for historical conditions results in somewhat moister soils at that time of the year. In the Swan River catchment, the relative change in LAI for the fine-resolution images indicates higher LAI values at current conditions, whereas the opposite is true for the coarse-resolution images. In the coarse-resolution images the vegetation at high elevations is assigned a higher LAI than in the current coarse-resolution image. In the fine-resolution images, these areas are classified as barren in both current and historical conditions, which explains the opposite shift in LAI for the coarse- and fine-resolution images. In any event, the ratios of current to historical LAI are quite close to one in both cases. In general, most of the model simulations show that annual average stream flow increases as LAI decreases. However, in the Swan River catchment the current stream-flow values are slightly higher than historical stream-flow values, despite higher LAI. This is a result of spatial patterns of LAI changes that are similar to those explained for Mores Creek. In Swan River, where the difference between current and historical LAI values is small, the stream flow and evapotranspiration during the summer and autumn are quite similar for current and historical conditions. The difference appears during snowmelt, at which time current stream-flow values are higher, and evapotranspiration lower, than for historical conditions. Mica Creek, which in 19 was covered by both conifers and deciduous trees at different growth stages, is now nearly entirely covered with mature second-growth forest. However, in the late 192s and 193s, more than 9% of Mica Creek was clear-cut logged, and subsequently burned. In Mica Creek, as well as in Mores Creek, snowmelt towards the end of the winter season is higher under current conditions than under historical conditions, which leads to increased soil moisture and higher peak flows during snowmelt. However, higher LAI values under current conditions lead to increased evapotranspiration early in the summer, which again lead to decreased soil moisture and somewhat lower peak flows later in the season. The decreased soil moisture at the end of the summer also results in decreased evapotranspiration values in Mica Creek and the western part of Mores Creek on 1 September (Figure 6).

16 2514 J. R. VANSHAAR, I. HADDELAND AND D. P. LETTENMAIER The LAI changes at Mica Potlatch are higher and spatially more uniform than the other historical vegetation changes studied, and the hydrological effects of the vegetation change is not influenced by subcatchment differences in the same way as for the other four studies. The majority of the stream-flow values at Mica Potlatch (not shown) are highest under clear-cut conditions, when snow water equivalent values are highest. In fact, only one of the selected peaks is predicted to be lower for 1933 than for 199 vegetation. The transition from clear-cut conditions to mature forest in Mica Creek Potlatch also results in the largest predicted changes in snow vapour flux (an increase of 44 mm or 3Ð1% of annual precipitation). Scale issues The four catchments modelled by DHSVM are represented by grid cells ranging in size from 3 m (Mica Creek) to 12 m (Mores Creek). As mentioned in the section Model implementation, these differences in scale are primarily a result of computational considerations. The spatial resolution might be expected to influence runoff production, as a higher resolution model represents topography, soil types and land cover in more detail than a lower resolution model. However, we feel that these differences in spatial resolution do not have a large effect on the resulting hydrological responses for two reasons. First, parameter estimation to some extent compensates for scale effects on the parameters (that is, the parameter estimation process is itself scale-dependent). As shown in Figure 2, the estimated parameters produce reasonable agreement between simulated and observed runoff. Second, the average number of contributing pixels to a stream channel is 15, 11, 5 and 3, for Mica Creek, Swan River, Entiat River and Mores Creek, respectively, meaning that the contributing area (15 : 44 : 45 : 48) to each stream channel is more similar than the pixel sizes (1 : 4 : 9 : 16 for Mica : Swan : Entiat : Mores). Runoff production in these four catchments is a result mainly of saturation excess mechanisms, which feed groundwater into the imposed channel network, meaning that stream density influences the form of the hydrographs more than does pixel size. A related question has to do with sensitivity of the results to the accuracy of the vegetation data. To address this question, a limited sensitivity study of simulated runoff to the scale and distribution of vegetation data was performed for Mores Creek. First, current vegetation at 12 m resolution was used to determine the dominant vegetation type and average canopy closure within 1 km 2 regions, and the combination of vegetation type and canopy closure was assigned for all 12 m cells within the region. Second, the vegetation types at 12 m resolution for historical conditions were distributed randomly within 1 km 2 regions. The change in runoff production at the basin outlet was less than 3% for both these studies. This suggests that the fraction of coverage of a given vegetation type over the catchment is more important than its spatial distribution when studying the accumulated runoff at the basin outlet, at least with some unknown range of proximity. This is encouraging, as accurate representation of the fraction of various vegetation cover types is an easier problem than is definitive specification of spatial locations at which various vegetation types are found certainly over the range of resolutions (3 to 12 m) used in this study. MODEL-PREDICTED HYDROLOGICAL EFFECTS OF CHANGES IN VEGETATION AGE Table IV summarizes the mean annual water balance for each study area for sensitivity scenarios in which all of the vegetation in the catchments is either Case 1, conifers at middle stage, or Case 2, conifers at early stage. The LAI for conifers at middle stage maturity is taken to be 1Ð5 times the LAI at early stage maturity (9 and 6, respectively). The increase in runoff when LAI decreases is around 2% for all catchments but Mores Creek. In Mores Creek, where annual runoff values are low, the relative change in runoff is high (c. 9%), but the absolute change is less in this catchment than in the others. In Entiat River, Swan River and Mica Creek, where precipitation is partitioned into somewhat similar amounts of evapotranspiration and stream flow, the change in evapotranspiration is around 2%. Evapotranspiration generally is highest where LAI is highest, especially when soil moisture is not limiting. As noted above, though, there can be a compensating

17 LAND COVER CHANGE EFFECTS 2515 Table IV. Simulated mean annual precipitation (P), stream flow (Q), evapotranspiration (ET) and snow vapour flux (S) P Q ETC S 1 1Q 1 ET C S Q ET C S (mm) Case 1 Case 1 Storage Case 1 Case 1 Case 1/ Case 1/ Case 2 Case 2 (mm) Case 2 Case 2 Case 2 Case 2 (mm) (mm) (mm) (mm) Mores C C C14 Ð53 1Ð21 Entiat C 46 C C 37 C5 127 C125 Ð82 1Ð3 Swan C C C141 Ð82 1Ð24 Mica C C C16 Ð8 1Ð25 effect where enhanced early season evapotranspiration occurs at the expense of evapotranspiration later in the season owing to soil moisture limitation. The effects of changes in LAI are more apparent in this sensitivity analysis than in the analysis of current and historical vegetation, where the hydrological effects resulted from the interaction of changes in LAI, the distribution of the vegetation and vegetation type, its location and whether overstory was present or not. In the all-conifer sensitivity tests, the differences in snow water equivalent can be explained more simply by the changes in LAI, and their interaction with topography. In all catchments, snow water equivalent is lower for middle conifer than for early conifer, and the difference is highest in areas where the fractional cover of conifer is highest (mostly at high elevations, except in barren areas above the tree line). Even within some of the high elevation areas with forest cover, there are subareas where the difference in snow water equivalent is rather low. There are even some apparently anamolous cases where the snow water equivalent is highest although the LAI is high counter to the general trend. These cases are attributable to topography, and mostly occur on south slopes where snow water equivalent is smaller than at northern slopes. Because a denser canopy decreases the energy fluxes that reaches the snow on ground, snow water equivalent can be higher despite higher LAI at current conditions. COMPARISON TO VIC RESULTS One of the objectives of this study was to determine whether the coarse-scale results of Matheussen et al. (2) obtained using the macroscale VIC model (1/4 spatial resolution) are consistent with those obtained using the topographically explicit DHSVM model. To compare the VIC and DHSVM results, the VIC pixels that comprise the four catchments studied here were extracted from an updated version of the VIC model (1/8, as reported by Miles et al. (2)). The resulting changes in stream flow and evapotranspiration associated with land-cover changes from historical conditions were compared with the results from DHSVM. The comparison indicated that VIC is less sensitive to changes in LAI than is DHSVM, but some of the differences uncovered are attributable to inconsistencies between the two models in meteorological forcing data and mean elevation in the catchments, mainly caused by the coarser spatial resolution in VIC. To obtain a better understanding of the relative sensitivities of VIC and DHSVM to vegetation change, VIC was implemented for Mores Creek and the Entiat River in a way that was designed to make the VIC and DHSVM representations as similar as possible in terms of model forcings, and representation of catchment physiography. Each VIC cell was given elevation, soil and vegetation parameters according to DHSVM s description within the VIC cell. One effect was that soil and rooting depths were decreased compared with the original (Matheussen et al., 2) VIC set-up, which is expected to have an influence on the hydrological responses (Mackay and Band, 1997). Up to six snow elevation bands were used in each VIC cell, which