The future condition analyses are for four climate scenarios representing a range of projected outcomes and model sensitivity to greenhouse gas forcing, and an analysis of the resulting hydrologic impacts. To develop confidence in the application of historic and future climate projections to hydrologic modeling, this paper evaluates the reliability of hydrologic model performance by comparing basin discharge, a product of the runoff and recharge values modeled by the BCM with basin discharge values measured at streamgages. We assembled historical streamgage data from 138 mostly unimpaired basins and used the monthly and yearly summaries from streamgages to test how well the BCM model outputs perform on watersheds with varying bedrock permeability, soil properties, impermeable surfaces, and degrees of aridity. The results of this model testing permit better interpretation of where hydrologic simulations perform better or worse due to influences of landscape variables. Tests were conducted by calculating discharge from BCM model outputs and comparing that to streamgage data.Global climate models are generally available for the continental United States at a 2.5 x 2.5 degree spatial resolution . A set of these projections have been down scaled to 1/8 x 1/8 degree spatial resolution for the State of California and its environs by researchers at USGS and Scripps Institute of Oceanography using the constructed analogs method of Hidalgo and others . These provide a basis for our further down scaling for model application. On the basis of analyses done by Cayan et al. ,25 liter plant pot several criteria were followed in the selection of models to downscale for the PIER V&A study. 

Models selected were required to produce a realistic simulation of aspects of California’s recent historical climate, particularly the distribution of monthly temperatures and the strong seasonal cycle of precipitation. They were required to contain realistic representation of some regional features, such as the spatial structure of precipitation, and similar degree of variability. In addition, models selected were to have differing levels of sensitivity to greenhouse gas forcing. As a result, for our analysis, two GCMs were selected: the Parallel Climate Model developed by National Center for Atmospheric Research and the U.S. Department of Energyand National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory CM2.1 model . The choice of greenhouse gas emissions scenarios included A2 and B1 , and was guided by considerations presented by the Intergovernmental Panel on Climate Change . Thus we developed a range of hydrology estimates based on four specific scenarios; two models each representing two emissions scenarios. We refer to these scenarios as “GFDL A2,” “GFDL B1,” “PCM A2,” and “PCM B1.” These four projections span the range of future climates in California and represent “warmer and wetter ,” and “much warmer and drier .” The above approach could be described as a precision‐based approach, which is one that, instead of examining all possible futures, focuses on projections thought to be most relevant for a particular area. An alternative that is frequently used in climate studies is the ensemble approach, wherein a stack of GCM projections or emission scenarios are considered in concert to determine consensus in the direction or magnitude of change . Consensus studies were conducted as part of the PIER V &A effort by other groups, including Krauchuk and Moritz .

This study uses the precision approach and illustrates a range of possible outcomes for projections previously described by Cayan et al. , and listed above. To put these scenarios in a larger context, the Krawchuk and Moritz paper comparesthese four scenarios to an ensemble of 16 models of global climate model output from the World Climate Research Programmeʹs Coupled Model Inter comparison Project phase 3 multi‐model dataset . In the 2010–2039 period, the GFDL falls in the mid‐range for temperature increases and low range for precipitation; the PCM falls in the low range for temperature and precipitation. By 2070–2099, the GFDL is still in the mid‐range among GCMs for temperature but among the lowest of the 16 GCMs for precipitation, thus supporting its use in representing a warmer, drier future. The PCM falls in the low range for temperature and low‐mid range for precipitation; annual precipitation shows little change, but higher amounts than the GFDL. Though by no means high‐ranking in precipitation amounts within the 16 GCM ensemble, the comparison to the GFDL supports its use in representing a warmer, wetter future.Climate projections are model simulations that describe potential future changes in climate.Unless bias corrected, GCM projections of current time climate often do not match measured current climate conditions. In order for future projections to match baseline climate, the entire record requires adjustment to establish the correct mean and variability of the baseline model simulations. The four scenarios of our study were therefore downscaled from the 12 km grid scale to the historical data scale of the Parameter‐ Elevation Regressions on Independent Slopes Model , by using the Gradient‐Inverse‐Distance‐Squared spatial interpolation approach . The purpose of this adjustment was to then apply a bias correction.

The bias correction brings the projected future conditions into alignment with baseline conditions, thereby making the calibration so that future conditions are tied to current climate levels. To make the correction possible, the GCM is run for an historical time period to establish a baseline for modeling to match baseline climate. The baseline period for this study is defined as the PCM and GFDL model runs for 1950–2000, when climate change forcings are assumed absent from the model, representing baseline atmospheric greenhouse gas conditions. This baseline period was then adjusted using the PRISM data from 1950–2000, for each month and for each grid cell. Our approach to bias correction is a simple scaling of the mean and standard deviation of the projections to match those of the PRISM data following Bouwer and others and described in detail in Flint and Flint . Once the bias correction is complete, the 4 km projections are further down scaled to 270 m spatial resolution using the GIDS spatial interpolation approach for model application.Development of data to run BCM for hydrologic climate change assessments required down scaling the climate inputs from 12 km grids to 270 m for historic and future projections. This included down scaling the PRISM data from 4 km2 to 270 m2 grids, and doing the same for the four future scenarios. We then had month‐by‐month and year‐by‐year data for minimum temperature , Maximum temperature , and precipitation . These were used as inputs for running the BCM to create the additional 11 hydrologic variables . Finally, we summarized each variable for six 30‐year time slices: 1911–1940, 1941–1970, 1971–2000, 2011–2040, 2041–2070, and 2071–2100, calculating the mean, standard deviation, rate of change within the 30‐year period,black plastic plant pots and several other values . Historical climate maps were derived from the empirically based PRISM monthly precipitation and air temperature database and maps that are available at 4 km spatial resolution . We down scaled the PRISM data, described below, to the 270 m operational grid scale for model application. All historical and future climate grids and maps of properties need to be at the same grid scale—in this case, 270 m—for model operation. Spatial down scaling was used to interpolate precipitation and air temperature grids from coarse‐scale grids to fine‐scale . The approach applies a spatial GIDS weighting to monthly point data by developing multiple regressions for every fine‐resolution grid cell for every month. Using the PRISM climate variables and the 4 km‐resolution digital elevation model, parameter weighting is based on the location and elevation of the coarse‐resolution cells surrounding each fine‐resolution cell to predict the climate variable of the fine‐resolution cell . To remove the “bullseye” effect often associated with certain interpolation schemes , the program was modified to have a search radius that is specified as the size of gridcell of the coarse‐resolution grid.

The modified GIDS spatial down scaling technique does not introduce additional uncertainty in the down scaling process, and may indeed improve the estimate of the climate variable by incorporating the deterministic influence of location and elevation on climate. The details of the methodology and the evaluation of uncertainty are discussed in Flint and Flint .The hydrology of a region is a function of the water balance, including the climatic input precipitation, and how it is partitioned into evapotranspiration; snow accumulation, sublimation, and melt; changes in soil moisture storage; and subsequently, runoff and recharge. Basin discharge can subsequently be calculated from runoff and recharge. The BCM mechanistically models the pathways of a basin’s precipitation into evapotranspiration, infiltration into soils, runoff, or percolation below the root zone to recharge groundwater. The evapotranspiration component of the BCM is derived through the use of potential evapotranspiration equations , solar radiation, slope and aspect, and topographic shading. For the purposes of comparison across watersheds , PET in the BCM is not interactive with the other segments. In other words, water demand from plants is independent from other hydrodynamic components in the model. The soil storage component of the model uses soil parameters to calculate how much water is available in the soil, a parameter of particular use to plant ecologists, and is also independent from the other major hydrologic dynamics, except that groundwater recharge, calculated as infiltration below the zone of evapotranspiration, is calculated only from surplus, after soil moisture capacity has been filled. Groundwater recharge is also tied to runoff, and the relationship between the two is driven by the level of permeability of bedrock.The hydrology of a region is a function of the water balance, including the climatic input precipitation, and how it is partitioned into evapotranspiration; snow accumulation, sublimation, and melt; changes in soil moisture storage; and subsequently, runoff and recharge. Basin discharge can subsequently be calculated from runoff and recharge. The BCM mechanistically models the pathways of a basin’s precipitation into evapotranspiration, infiltration into soils, runoff, or percolation below the root zone to recharge groundwater. The evapotranspiration component of the BCM is derived through the use of potential evapotranspiration equations , solar radiation, slope and aspect, and topographic shading. For the purposes of comparison across watersheds , PET in the BCM is not interactive with the other segments. In other words, water demand from plants is independent from other hydrodynamic components in the model. The soil storage component of the model uses soil parameters to calculate how much water is available in the soil, a parameter of particular use to plant ecologists, and is also independent from the other major hydrologic dynamics, except that groundwater recharge, calculated as infiltration below the zone of evapotranspiration, is calculated only from surplus, after soil moisture capacity has been filled. Groundwater recharge is also tied to runoff, and the relationship between the two is driven by the level of permeability of bedrock.The unique response of any given watershed to climate is primarily related to its energy balance , soil moisture storage capacity, and the characteristics of the materials that are deeper than the rooting zone of vegetation, including deep alluvial valleys or channels or bedrock that lead to deep percolation into the groundwater system. The monthly water balance can be calculated using the BCM that was originally developed for arid and semi‐arid lands with minimal stream flow data upon which rainfall‐ runoff models rely for calibration . The BCM calculates hydrologic variables on a grid cell basis and can be run at any spatial resolution, generally limited by computing power or file storage capabilities. For this application we developed a spatial resolution of 270 m, not so fine‐scale to be computationally prohibitive, but fine enough to capture differences on hillslopes due to variable radiation and soil properties. Grid cell values can be summarized for any spatial pattern, here using the USGS hydrologic unit code 12 watersheds.The BCM has a number of subroutines or modules. Figure 2 is a schematic of the processes and calculations addressed in the BCM. This is followed by the calculation of PET. This calculation relies on an hourly energy‐balance calculation that is based on solar radiation, air temperature, and the Priestley‐Taylor equation . Clear sky PET is calculated using a solar radiation model that incorporates seasonal atmospheric transmissivity parameters and site parameters of slope, aspect, and topographic shading . Hourly PET is aggregated into monthly time series, and cloudiness corrections are made using cloudiness data from National Renewable Energy Laboratory .