Generally basins with the least impairments had the best calibrations, with the exception of basins in the volcanics in the upper Klamath River basin. Generally calibration and performance results for the BCM were satisfactory in basins throughout the state with both unimpaired and impaired basins providing close matches of estimated basin discharge to measured stream flow. Basins with the lowest BCM performance scores contained unaccounted‐for land uses, such as agricultural or municipal diversions or return flows, or water impoundments such as reservoirs. These results provide reasonable confidence in the spatially distributed estimates of recharge, runoff, and climatic water deficit throughout the entire state, as well as for ungaged basins. However, runoff in the BCM is not routed, and basin discharge requires post‐processing using measured stream flow to determine the relative contributions of recharge and runoff in a basin to gains and losses in stream flow. Once established, these components can be used to extrapolate basin discharge through time, given no changes in impairments. Uncertainties in calibration do not affect estimates of CWD, as this calculation does not rely on the bedrock permeability used to partition the excess water into recharge and runoff. Soil water conditions are a function of soil properties, available water from precipitation and evapotranspiration. Therefore, the BCM calculation of CWD, PET minus actual evapotranspiration , reflects the uncertainties inherent in the climate data, in the soil properties from soil mapping and PET. The BCM is a physically based,round pot deterministic model that provides information at a fine scale on the basis of down scaled climate input data and the most detailed maps available to represent other landscape variables used.
These techniques have been tested to illustrate accuracy in representing the larger‐scale climate datasets . The calculation of potential evapotranspiration has been rigorously developed and calibrated to measured data throughout California . Applications of the BCM to determine hydrologic response, as measured by discharge to climate, rely on maps of soil type and estimates of properties and geologic maps from which bedrock permeability is estimated. Soil properties are maintained static, but bedrock permeability is adjusted to change the ratio of recharge to runoff and improve the comparison of estimated total basin discharge to measured stream flow. If a geologic type is mapped as one type over a broad area of the state, when bedrock permeability is adjusted to improve calibration in one location, the relative proportions of recharge and runoff change correspondingly across the domain in basins with similar geologic types. Since discharge is derived from runoff and recharge, a post‐processing calibration/validation may be conducted, as was the case in this study.A useful application of the BCM beyond the estimates of spatially distributed recharge and runoff would be to estimate basin discharge at ungaged basins. To this end, there were attempts made to correlate the basin calibration parameters that were used to adjust the basin discharge to match measured stream flow in gaged basins to landscape variables. The intent was to enable the extrapolation of discharge estimates to ungaged basins on the basis of mapped physical characteristics such as geology, soil properties, slope, basin area, or aridity. The concept here is that parameters that adjusted the base flow to be high in basins with high bedrock permeability, for example, might have a correlation across multiple basins. However, none of these showed a significant relationship with model calibration parameters across all calibration and validations in California.
Possible reasons for the lack of relationship include potential errors in the soils or geology maps, or in the PRISM climate data, or due to human activities that are affecting basin hydrology at the watershed scale. Since the BCM is a mechanistic model, driven by a series of physically based assumptions, we argue that the model output is of value for regional comparisons of watersheds, even in the absence of independent validation for ungaged basins. If we assume that the properties and climate are correct, the BCM hydrologic outputs are based on properties that are spatially distributed throughout the study area, and the calculations performed consistently across all basins, providing a level of confidence when using the hydrologic results for regional cross‐comparisons of basins.Some of the sources of error are well known to geographers. Soils maps are particularly prone to error, since accurate measures of soil depth are difficult to obtain, and currently unobtainable for large areas. The national State Soil Geographic dataset smoothes out landscape features and generally disregards topographic controls on soil depth. County‐level soils maps are currently being developed for the state for application to the BCM, which will provide much better accuracy in BCM output, particularly for CWD. Improving measurements of soil depth generally emerged as one of the most important data development agendas as a result of this study.Human activities are extensive in California, and likely have impacts in nearly every basin. These activities that can affect the hydrologic cycle at the watershed scale include small impoundments, direct pumping from streams for urban or agricultural use, construction of impermeable surfaces, and changes to the natural land cover. These can affect variously the partitioning of input PPT to different pathways in the hydrological cycle, and also affect the actual evapotranspiration and PET calculated as part of the model.
We were not able to make detailed efforts on assessing the influence of human activities on the overall model accuracy, and feel this would be a suitable agenda for future research, particularly for basins with several gages that are placed below and above areas of human disturbance. The historical climate used for this analysis, PRISM, relies on meteorological station data, and empirical relations of numerous factors affecting the local expression of climate on the landscape, including distance from the ocean, extent of mountain ranges, and other features, to interpolate among data locations. This dataset is therefore a model, relying on data, and does not strictly honor the measured data in most locations. However, the interpolated data likely reflects better estimates of climate in locations without measurements than nearby station data at different elevations or topographic settings, and is considered a useful and competent long‐ term dataset. The estimate of spatially distributed runoff does not equal basin discharge as measured at a streamgage without post‐processing to determine the components of runoff and recharge that contribute to stream channel gains and losses, which must be done using some measured data for a given basin. The resultant parameters corresponding to the gains and losses generally reflect climatic conditions and geologic setting,round plastic planter but at the scale of hydrologic California have not been determined to a degree that allows for the direct extrapolation of basin discharge to all ungaged basins. The spatial distribution of runoff and recharge, however, provide relative differences over the region and indicate the differences in sensitivity of basins to changes in climate. The estimate of changes in soil moisture and CWD do not rely on interpretation of bedrock permeability, and uncertainties correspond more closely with those of the mapped soil properties and climate data. Because the BCM model outputs are calculated on a grid‐cell basis, results can be summarized across landscapes using summary units of any size of interest such as watersheds, ecoregions, or political boundaries. The ability to spatially project hydrological model outputs permits the cross‐comparison of these landscape delineations, with mapped outputs of interest to various fields of research . The discharge and groundwater outputs can inform water management for storage and human consumption, and anadromous fisheries. Soil moisture and climatic water deficit are of interest for tracking suitability of rain‐fed agriculture and for assessment of suitability of natural environments for component plants and animals. The ability to calculate hydrological outputs using a transparent, mechanistic approach, and at fine spatial scales, permits a new set of predictor variables to be used in the spatial projection of suitable plant ranges or habitats . This is a particularly important opportunity for ecologists and conservation biologists because species distribution models are one the primary methods of evaluating the susceptibility of species to climate change .
Natural resource managers and field ecologists are particularly interested in this variable, as it integrates site conditions with temperature and moisture, and is therefore a factor that plants may respond to more directly than climate variables alone, particularly in regions with pronounced seasons. The strength of the BCM in portraying CWD is that different watersheds can be compared by identifying the area‐weighted mean value. Therefore relative differences across hydrologic California are comparable. The down scaling of historical climate data and future climate scenarios for application to the BCM to calculate hydrologic response to climate change has provided a dataset that is both rich in its regional representation of climatic and hydrologic trends, but also spatially detailed to provide fine‐scale examples of local impacts of climate change on the landscape.Landscape responses to climate change are moderated by locations with lower energy loads, such as north‐facing hillslopes or coastal regions with frequent cloud cover. Soil also amplifies or moderates the hydrologic response for the landscape to climate change, depending on whether soils are thin and excess water is lost to runoff or recharge, or if they are thick and can maintain moisture longer into the season. Mountainous regions seasonally occupied by snow pack are quite sensitive to climate change, as the timing of snowmelt is enhanced by warming, thus changing the length of the wet season and extending the dry season for all regions downstream relying on snow pack for public and agricultural use. The BCM, using the best map data available, still shows that we have not captured all the details that drive individual watershed dynamics. However, for comparative purposes across a large number of watersheds and ecoregions, the relative consistency of the model permits informative interpretations. This is, in essence, very similar to the way in which GCMs themselves run, in that they provide a platform for intercomparison of regions even while they may be more or less accurate when compared to ground‐based measurements. In this regard, then, the next challenge for modelers of these physical processes is to determine how to incorporate the next‐finer scale of detail. Basin Characterization Model output maps indicate where on the landscape changes in the hydrologic cycle occur. If discharge or recharge boundary conditions are needed at the watershed‐scale basin, then this study suggests that some local calibration is necessary. However, if the basins are purely unimpaired, then nearby or adjacent basin calibration is likely to suffice.The consistent patterns offered by the BCM for a variety of hydroclimatic variables that relate to ecological processes to fine spatial scales make the model output of particular interest to landscape ecologists, and to those interested in modeling the bio-geographic response of species and vegetation types to future changes in climate. Part of the interest derives from the fact that future moisture conditions are much more difficult to project than future temperature; a fact that emerges when comparing the outputs of future GCMs for temperature and precipitation, where there is much higher agreement between models for temperature. Having a mechanistic model that captures the dynamics of the water that is predicted permits a better estimation of hydrological conditions under varying scenarios, which in turn can provide a view to the range of potential impacts to water available for natural processes and for human uses; particularly rain‐fed agriculture.Because of the modular nature of the Basin Characterization Model, it is possible to make two types of improvements. First, any particular module’s calculations may be updated and improved. An example would be if PET values for different vegetation types could be calculated, these could be applied using an existing vegetation map to render more accuracy in the plant‐driven parts of the model. Second, input data maps may be updated and improved.Refinements are currently planned to improve the accuracy of the spatially distributed outputs, and improve confidence in estimates in ungaged basins. On the basis of a comparison the PRISM 4 km‐resolution climate data used in this study, and the subsequently available PRISM 800 m‐resolution climate data , map‐based improvements for the next version of BCM projections include the incorporation of the PRISM 800 m transient climate dataset for historical BCM analyses.