In recent decades, warmer temperatures in the western United States have led to a reduction of snow accumulation as well as earlier melt and streamflow [1
]. Changing snowmelt input has also altered the timing and magnitude of soil moisture, vegetation water use and productivity [3
]. A variety of hydrological models have been used to assess the effect of climate change on the ecohydrologic response at various watershed scales [1
]. However, spatial units in these models tend to be defined at relatively coarse spatial resolutions (>100 m) and thus ignore the fine-scale variation of topography. Particularly in mountain environments, substantial variation in topographic properties over relatively short spatial scales is observed, and the distribution of atmospheric forcing variables (radiation, temperature and precipitation), and local and lateral moisture are often related to this fine-scale variation in topography. Therefore, ignoring the fine-scale variation of mountain topography may result in poor predictions of ecohydrologic responses to climate change for small watersheds.
Previous studies have emphasized the importance of detailed topographic information for characterizing hydrologic and geomorphic properties of watersheds and obtaining accurate hydrologic and ecologic predictions [6
]. Cline et al. [7
] showed that the mean snow water equivalent (SWE) predictions using a 90 m digital elevation model (DEM) are different from the predictions obtained using a 30 m DEM in the Emerald Lake watershed in California. Zhang and Montgomery [6
] showed that TOPMODEL [9
] using a DEM with 10 m resolution improved streamflow predictions compared to simulations using coarser DEM (30 m and 90 m) for two small catchments in the western United States. Lassueur et al. [8
] demonstrated the usefulness of a fine-resolution DEM to estimate plant species richness in an alpine landscape.
Studies evaluating the response of model performance to DEM resolution show that sensitivity varies across sites. Kuo et al. [10
] showed that model estimates for slowly undulating landscapes tend to be less degraded with increasing grid size than those for landscapes with steep valleys. Their research also found that runoff does not change with grid size in wet years, but does change in dry years. Model predictions for snow-dominated watersheds may be more sensitive to DEM resolution than those for rain-dominated watersheds because topographic parameters (elevation, aspect and slope) determine the energy input, thereby controlling the snow melt patterns [11
]. DEM resolution also affects the snow accumulation estimates because many snow models use a simple lapse rate based on air temperature and elevation to partition the total precipitation into snow and rain.
The effect of DEM resolution on model predictions also varied with the variable of interest [13
]. Using Soil and Water Assessment Tool (SWAT) modeling, coarsened DEM resolution was found to reduce the accuracy of both streamflow and NO3
-N load prediction, but not the accuracy of total P load predictions [13
]. A distributed hydrologic model used to predict average soil moisture and streamflow at the hillslope scale showed that using a coarser DEM did not reduce model accuracy, but the spatial pattern of soil moisture was distorted [14
]. Estimates from a distributed ecohydrologic model showed that the grid-size effect on net primary productivity (NPP) estimates is more significant than on evapotranspiration (ET) estimates [15
These previous studies have focused on the effect of DEM resolution on model predictions in general. However, the importance of fine-scale topographic variation in hydrologic modeling for climate change studies and other issues is not well understood. The declines in accuracy with coarsening resolution noted above may or may not be critical for using models to make inferences about climate change effects. Vegetation water and productivity are important variables for assessing the effect of climate change on ecosystem productivity, but previous hydrologic studies do not integrate the effect of DEM resolution on changes in water availability and the related impacts on modeled ET and NPP.
This study evaluated the effect of DEM resolution on the accuracy of modeled streamflow, specifically for rain-snow transition watersheds and snow-dominated watersheds that are expected to be particularly sensitive to climate change. This study also explicitly tested how DEM resolution influences the sensitivity of modeled ecohydrologic responses (annual streamflow, summer flow, annual ET and annual NPP) to inter-annual climate variability. Investigation of the influences of DEM resolution on the estimates of ecohydrologic responses to historic climate variability serves as an indicator of the likely importance of DEM resolution for future predictions.
The Regional Hydrologic-Ecologic Simulation System (RHESSys) [16
] was applied to eight small Sierra Nevada watersheds. The watersheds have different dominant precipitation phases (snow vs. rain), topographic properties (elevation, slope and aspect), and vegetation properties (leaf area index, rooting depths). This study answers three questions: (1) does the total precipitation phase (snow vs. rain) control the sensitivity of model estimates to DEM resolution; (2) which topographic parameters determine the sensitivity of model estimates to DEM resolution; and (3) which variable of interest among model estimates is the most sensitive to DEM resolution? Model estimates consider both annual means and inter-annual variation in ecohydrologic variables. To answer these questions, this study follows the framework outlined in Figure 1
. First, this study investigates the effect of DEM resolution on topographic parameters (elevation, slope, aspect and wetness index) in the eight watersheds. Second, this study identifies the watershed sensitivity based on the difference in estimates of the snow water equivalent (SWE), and the accuracy of modeled daily streamflow among various resolution models. Finally, this study estimates the sensitivity of the model estimates of the four ecohydrologic variables (annual streamflow, summer streamflow, annual ET and annual NPP) to DEM resolution. These tests provide a guideline for determining the appropriate DEM resolution in ecohydrologic modeling for climate effect assessment for the Sierra Nevada watersheds.
5. Discussion and Summary
This study was performed to improve our understanding of how DEM resolution affects ecohydrologic estimates in the context of using a model to evaluate climate change effects in small mountain watersheds. Three hypotheses were posed to test the DEM sensitivity within the TSW and SDW groups of watersheds and among the variables of interest: (1) model estimates for transient snow watersheds (TSWs) will have a higher sensitivity to DEM resolution than the model estimates for snow-dominated watersheds (SDWs); (2) changes in the spatial variation of the wetness index will explain the watershed sensitivity to DEM resolution; and (3) flow estimates will be more sensitive to DEM resolution than ET and NPP estimates.
This study showed that there is a clear threshold resolution (10 m) above which coarser resolutions have large effects on streamflow prediction accuracy (Figure 7
). Among the eight watersheds, TSWs tend to have both a lower streamflow accuracy and a larger reduction of streamflow accuracy with coarsening DEM resolution (Table 3
). Among TSWs, streamflow accuracy for P304 and D102 is the most sensitive to DEM resolution, but P301 is the second least-sensitive watershed to DEM resolution between the eight watersheds. The first hypothesis, that sensitivity to DEM resolution is closely linked to snow accumulation and melt characteristics, is not supported. The change in peak SWE with coarsening DEM is very minor for all eight watersheds. P301 with the lowest sensitivity to DEM resolution has the largest change in watershed absolute difference in SWE between 5 m and 150 m (Figure 5
). Thus, the difference in the dominant precipitation phase between TSWs and SDWs does not lead to consistent differences in the sensitivity of flow estimates to changes in the model resolution.
Among topographic parameters, we hypothesized that the change in the spatial variation of the wetness index can explain the watershed sensitivity to DEM resolution. Changing the spatial variance of the wetness index has a complex relationship with coarsening DEM, and varies between watersheds. However, the lowering in the spatial variance of the wetness index with coarsening DEM corresponds with a reduction of the streamflow accuracy (Table 3
). For example, when the 5 m resolution model was compared with coarser resolution models, P301 and D102 had the smallest reduction (−9%) and the largest reduction (−26%) of the spatial variance of the wetness index, respectively, which corresponds to the smallest and largest reductions of streamflow accuracy for the watersheds (−25% and −71%, respectively). Among the eight watersheds, T003 has the smallest reduction (−15%) of the streamflow accuracy, and that watershed shows an increase (11%) of the spatial variance of the wetness index. RHESSys does not use the wetness index directly to calculate lateral flow. However, the wetness index includes the component of topographic slope and flow-accumulating area. RHESSys actually uses these components to determine the lateral flow paths. Previous studies using TOPMODEL [10
] also showed that decreasing resolution reduces the spatial variance of the wetness index [6
]. Pradhan et al. [34
] showed that when a coarser DEM resolution (1000 m) reproduced the cumulative distribution of the wetness index at the fine resolution (50 m), the streamflow estimates using the coarser 1000 m DEM resolution matched the simulated streamflow in the 50 m DEM resolution TOPMODEL without recalibration. Results in this study suggest that the change in the wetness index distribution will also be a good indicator of whether coarsening the DEM will lead to reduced accuracy for an explicit routing model. Kenward et al. [31
] tested the impact of DEM resolution on the streamflow accuracy and spatial pattern of a predicted saturated area using DHSVM [35
] which has a similar routing scheme to RHESSys. Their study also showed that the spatial distribution of the wetness index corresponded to the depth to saturation and runoff production for a rain-dominated system in the WF-38 experimental watershed at the Mathantango Creak, PA. Our study confirms that the impact of DEM resolution on flow paths is also likely to be important for snow and rain-snow transition watersheds and that the impact of model resolution on the lateral redistribution of water may be more important than its impact on snow accumulation and melt for models of low-order, headwater watersheds.
Among the model accuracy measures, PerErr has the highest sensitivity to DEM resolution. Changes in PerErr are directly related to changes in annual ET. We note that annual ET estimates and their COV are strongly sensitive to DEM resolution (Figure 8
and Figure 9
). Changes in the wetness index distribution may also be important in ET estimates, particularly in water-limited environments. The impact of DEM resolution on ET is discussed in more detail below.
Among the model estimates, we hypothesized that the flow estimate to DEM resolution will be more sensitive than ET and NPP estimates. Our modeling results found that among the four ecohydrologic estimates of interest, DEM resolution has the largest effects on the mean summer flow and COV of the annual ET and NPP (Figure 7
and Figure 8
). One of the eight watersheds, T003 had the smallest reduction in streamflow accuracy with coarsening DEM, but large changes in the mean summer flow (150%), the COV of the annual ET (65%), and the COV of the annual NPP (60%) are observed. These results emphasize that accurate streamflow prediction does not guarantee a model’s ability to capture long-term ecohydrologic responses to climate change. Our study also suggests that using a fine-resolution DEM in ecohydrologic modeling is essential in order to capture the long-term observed summer flow. Since summer flow is an important water resource and has substantial implications for aquatic organisms in California, fine-scale hydrologic modeling for assessing the effect of climate change in Sierra Nevada is necessary [36
Our modeling study showed that a coarsening DEM resolution results in an increase in the COV of both ET and NPP. This result implies that coarser-resolution models overestimate the sensitivity of these processes to climate variation. This result is important for interpreting and predicting ecosystem responses to climate change. The reduced sensitivity of ET and NPP for the finer-resolution models may be related to the substantial variation in topographic properties in mountain environments. The high variation in topographic properties may lead to spatial variation in the sizes of water storage and flow path convergence. As discussed above, coarsening the DEM tends to reduce spatial variation in the wetness index. The vegetation response to changing climate may be lower for the finer-resolution model because this spatial variation in water storage and flow path convergence provides additional opportunities for plants to access water. A higher-resolution DEM, for example, may lead to greater areas of local flow path convergence typified by riparian areas and local depressions with greater soil moisture. ET in these areas may be less sensitive to inter-annual climate variation. The higher COV of ET and NPP with coarsening DEM resolution may also illustrate the role of micro-refuge created by substantial variation in other topographic properties in mountain environments [37
]. Dobrowski et al. [37
] provide case studies where terrain allows for local climate conditions to be decoupled from the regional climate; when sites decouple from the regional climate, micro-refuges can occur for species. The finer-resolution model may create microclimate conditions, as well as areas of increased moisture storage, that are less sensitive to the forcing climate variability.
In summary, this study demonstrates that using fine-scale DEM in ecohydrologic modeling influences the accuracy of streamflow estimation in headwater mountain catchments and substantially alters estimates of climate-driven inter-annual variation in ET and NPP in these systems. Results emphasize that these effects may be largely due to the role of the DEM in the model estimation of hydrologic flowpaths rather than the model estimation of snow accumulation and melt. This study found that coarser-resolution models tend to have lower streamflow accuracy and overestimate climate sensitivity for ET and NPP. These results have important implications for model-based studies used to assess ecosystem responses to climate change, and, in particular, caution that coarser-resolution models may overestimate climate sensitivity. The analysis, however, demonstrates a non-linear relationship between model accuracy/sensitivity and DEM resolution and suggests that increasing resolution from 30 m to 10 m makes substantial improvements. Further increasing the resolution to 5 m results in smaller gains in performance, relative to the increase in computation cost.