Civil Engineering

Global climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction methods, and a new one developed here, are applied to daily maximum temperature and precipitation from 21 GCMs to investigate how different methods alter the climate change signal of the GCM. The quantile mapping (QM) and cumulative distribution function transform (CDF-t) bias correction methods can significantly alter the GCM's mean climate change signal, with differences of up to 28C and 30% points for monthly mean temperature and precipitation, respectively. Equidistant quantile matching (EDCDFm) bias correction preserves GCM changes in mean daily maximum temperature but not precipitation. An extension to EDCDFm termed PresRat is introduced, which generally preserves the GCM changes in mean precipitation. Another problem is that GCMs can have difficulty simulating variance as a function of frequency. To address this, a frequency-dependent bias correction method is introduced that is twice as effective as standard bias correction in reducing errors in the models' simulation of variance as a function of frequency, and it does so without making any locations worse, unlike standard bias correction. Last, a preconditioning technique is introduced that improves the simulation of the annual cycle while still allowing the bias correction to take account of an entire season's values at once.

This paper describes a publicly available, long-term (1915–2011), hydrologically consistent dataset for the con-terminous United States, intended to aid in studies of water and energy exchanges at the land surface. These data are gridded at a spatial resolution of 1 /168 latitude/longitude and are derived from daily temperature and precipitation observations from approximately 20 000 NOAA Cooperative Observer (COOP) stations. The available meteorological data include temperature, precipitation, and wind, as well as derived humidity and downwelling solar and infrared radiation estimated via algorithms that index these quantities to the daily mean temperature, temperature range, and precipitation, and disaggregate them to 3-hourly time steps. Furthermore, the authors employ the variable infiltration capacity (VIC) model to produce 3-hourly estimates of soil moisture, snow water equivalent, discharge, and surface heat fluxes. Relative to an earlier similar dataset by Maurer and others, the improved dataset has 1) extended the period of analysis (1915–2011 versus 1950–2000), 2) increased the spatial resolution from 1 /88 to 1 /168, and 3) used an updated version of VIC. The previous dataset has been widely used in water and energy budget studies, climate change assessments, drought reconstructions, and for many other purposes. It is anticipated that the spatial refinement and temporal extension will be of interest to a wide cross section of the scientific community.

The Californian Mono Lake Basin (MLB) is a fragile ecosystem, for which a 1983 ruling carefully balanced water diversions with ecological needs without the consideration of global climate change. The hydroclimatologic response to the impact of projected climatic changes in the MLB has not been comprehensively assessed and is the focus of this study. Downscaled temperature and precipitation projections from 16 Global Climate Models (GCMs), using two emission scenarios (B1 and A2), were used to drive a calibrated Soil and Water Assessment Tool (SWAT) hydrologic model to assess the effects on streamflow on the two significant inflows to the MLB, Lee Vining and Rush Creeks. For the MLB, the GCM ensemble output suggests significant increases in annual temperature, averaging 2.5 and 4.1 °C for the B1 and A2 emission scenarios, respectively, with concurrent small (1– 3 %) decreases in annual precipitation by the end of the century. Annual total evapotrans-piration is projected to increase by 10 mm by the end of the century for both emission scenarios. SWAT modeling results suggest a significant hydrologic response in the MLB by the end of the century that includes a) decreases in annual streamflow by 15 % compared to historical conditions b) an advance of the peak snowmelt runoff to 1 month earlier (June to May), c) a decreased (10–15 %) occurrence of 'wet' hydrologic years, and d) and more frequent (7–22 %) drought conditions. Ecosystem health and water diversions may be affected by reduced water availability in the MLB by the end of the century.

Central Chile is facing dramatic projections of climate change, with a consensus for declining precipitation, negatively affecting hydropower generation and irrigated agriculture. Rising from sea level to 6000 m within a distance of 200 km, precipitation characterization is difficult because of a lack of long-term observations, especially at higher elevations. For understanding current mean and extreme conditions and recent hydro-climatological change, as well as to provide a baseline for downscaling climate model projections, a temporally and spatially complete dataset of daily meteorology is essential. The authors use a gridded global daily meteorological dataset at 0.258 resolution for the period 1948–2008, adjusted by monthly precipitation observations interpolated to the same grid using a cokriging method with elevation as a covariate. For validation, daily statistics of the adjusted gridded precipitation are compared to station observations. For further validation , a hydrology model is driven with the gridded 0.258 meteorology and streamflow statistics are compared with observed flow. The high elevation precipitation is validated by comparing the simulated snow extent to Moderate Resolution Imaging Spectroradiometer (MODIS) images. Results show that the daily meteorology with the adjusted precipitation can accurately capture the statistical properties of extreme events as well as the sequence of wet and dry events, with hydrological model results displaying reasonable agreement with observed streamflow and snow extent. This demonstrates the successful use of a global gridded data product in a relatively data-sparse region to capture hydroclimatological characteristics and extremes.

Precipitation from the Eastern Sierra Nevada watersheds of Owens Lake and Mono Lake is one of the main water sources for Los Angeles' over 4 million people, and plays a major role in the ecology of Mono Lake and of these watersheds. We use the Variable Infiltration Capacity (VIC) hydrologic model at daily time scale, forced by climate projections from 16 global climate models under greenhouse gas emissions scenarios B1 and A2, to evaluate likely hydrologic responses in these watersheds for 1950–2099. Comparing climate in the latter half of the 20th Century to projections for 2070–2099, we find that all projections indicate continued temperature increases, by 2–5 °C, but differ on precipitation changes, ranging from −24 % to +56 %. As a result, the fraction of precipitation falling as rain is projected to increase, from a historical 0.19 to a range of 0.26–0.52 (depending on the GCM and emission scenario), leading to earlier timing of the annual hydrograph's center, by a range of 9– 37 days. Snowpack accumulation depends on temperature and even more strongly on precipitation due to the high elevation of these watersheds (reaching 4,000 m), and projected changes for April 1 snow water equivalent range from −67 % to +9 %. We characterize the watershed's hydrologic response using variables integrated in space over the entire simulated area and aggregated in time over 30-year periods. We show that from the complex dynamics acting at fine time scales (seasonal and sub-seasonal) simple dynamics emerge at this multi-year time scale. Of particular interest are the dynamic effects of temperature. Warming anticipates hydrograph timing, by raising the fraction of precipitation falling as rain, reducing the volume

s u m m a r y Due to global warming the climate of central Chile is expected to experience dramatic changes in the 21st century including declining precipitation, earlier streamflow peaks, and a greater proportion of precipitation falling as rain. We used 12-member ensembles of General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5) to evaluate climate-attributed changes in the hydrology of the Mataquito river basin in central Chile, South America. Simulations using the Variable Infiltration Capacity (VIC) hydrology model indicate that a drier and warmer future will shift the location of snow line to higher elevations and reduce the number of days with precipitation falling as snow. Extreme precipitation and streamflow events are expected to become more frequent. Conversely, low flow conditions will intensify during the warm months. The changes in the mean of hydrologic states and fluxes by the end of the 21st century are statistically robust, whereas changes in the variance are not found to be statistically significant. Results of the ensembles for CMIP3 and CMIP5 are generally indistinguishable regarding projected impacts on hydrology.

s u m m a r y Downscaled and hydrologically modeled projections from an ensemble of 16 Global Climate Models suggest that flooding may become more intense on the western slopes of the Sierra Nevada mountains, the primary source for California's managed water system. By the end of the 21st century, all 16 climate projections for the high greenhouse-gas emission SRES A2 scenario yield larger floods with return periods ranging 2–50 years for both the Northern Sierra Nevada and Southern Sierra Nevada, regardless of the direction of change in mean precipitation. By end of century, discharges from the Northern Sierra Nevada with 50-year return periods increase by 30–90% depending on climate model, compared to historical values. Corresponding flood flows from the Southern Sierra increase by 50–100%. The increases in simulated 50 year flood flows are larger (at 95% confidence level) than would be expected due to natural variability by as early as 2035 for the SRES A2 scenario.

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.

[1] We develop a stream temperature model within the Soil and Water Assessment Tool (SWAT) that reflects the combined influence of meteorological (air temperature) and hydrological conditions (streamflow, snowmelt, groundwater, surface runoff, and lateral soil flow) on water temperature within a watershed. SWAT currently uses a linear air-stream temperature relationship to determine stream temperature, without consideration of watershed hydrology. As SWAT uses stream temperature to model various in-stream biological and water quality processes, an improvement of the stream temperature model will result in improved accuracy in modeling these processes. The new stream temperature model is tested on seven coastal and mountainous streams throughout the western United States for which high quality flow and water temperature data were available. The new routine does not require input data beyond that already supplied to the model, can be calibrated with a limited number of calibration parameters, and achieves improved representation of observed daily stream temperature. For the watersheds modeled, the Nash-Sutcliffe (NS) coefficient and mean error (ME) for the new stream temperature model averaged 0.81 and À0.69 C, respectively, for the calibration period and 0.82 and À0.63 C for the validation period. The original SWAT stream temperature model averaged a NS of À0.27 and ME of 3.21 C for the calibration period and a NS of À0.26 and ME of 3.02 C for the validation period. Sensitivity analyses suggest that the new stream temperature model calibration parameters are physically reasonable and the model is better able to capture stream temperature changes resulting from changes in hydroclimatological conditions.

We compare the projected changes to streamflows for three Sierra Nevada rivers using statistically downscaled output from 22 global climate projections. The downscaled meteorological data are used to drive two hydrology models: the Sacramento Soil Moisture Accounting model and the variable infiltration capacity model. These two models differ in their spatial resolution, computational time step, and degree and objective of calibration , thus producing significantly different simulations of current and future streamflow. However, the projected percentage changes in monthly streamflows through mid-21st Century generally did not differ, with the exceptions of streamflow during low flow months, and extreme low flows. These findings suggest that for physically based hydrology models applied to snow-dominated basins in Mediterranean climate regimes like the Sierra Nevada, California, model formulation, resolution, and calibration are secondary factors for estimating projected changes in extreme flows (seasonal or daily). For low flows, hydrology model selection and calibration can be significant factors in assessing impacts of projected climate change. (KEY TERMS: climate variability ⁄ change; recharge; runoff; surface water hydrology.) Maurer, Edwin P., Levi D. Brekke, and Tom Pruitt, 2010. Contrasting Lumped and Distributed Hydrology Models for Estimating Climate Change Impacts on California Watersheds.

Sierra Nevada snowmelt and runoff is a key source of water for many of California's 38 million residents and nearly the entire population of western Nevada. The purpose of this study was to assess the impacts of expected 21st Century climatic changes in the Sierra Nevada at the subwatershed scale, for all hydrologic flow components, and for a suite of 16 General Circulation Models (GCMs) with two emission scenarios. The Soil and Water Assessment Tool (SWAT) was calibrated and validated at 35 unimpaired streamflow sites. Results show that temperatures are projected to increase throughout the Sierra Nevada, whereas precipitation projections vary between GCMs. These climatic changes drive a decrease in average annual streamflow and an advance of snowmelt and runoff by several weeks. The largest streamflow reductions were found in the mid-range elevations due to less snow accumulation, whereas the higher elevation watersheds were more resilient due to colder temperatures. Simulation results showed that decreases in snowmelt affects not only streamflow, but evapotranspiration, surface, and subsurface flows, such that less water is available in spring and summer, thus potentially affecting aquatic and terrestrial ecosystems. Declining spring and summer flows did not equally affect all subwatersheds in the region, and the subwatershed perspective allowed for identification for the most sensitive basins throughout the Sierra Nevada.

Climate model simulations disagree on whether future precipitation will increase or decrease over Cal-ifornia, which has impeded efforts to anticipate and adapt to human-induced climate change. This disagreement is explored in terms of daily precipitation frequency and intensity. It is found that divergent model projections of changes in the incidence of rare heavy (.60 mm day 21) daily precipitation events explain much of the model disagreement on annual time scales, yet represent only 0.3% of precipitating days and 9% of annual precipitation volume. Of the 25 downscaled model projections examined here, 21 agree that precipitation frequency will decrease by the 2060s, with a mean reduction of 6–14 days yr 21. This reduces Cal-ifornia's mean annual precipitation by about 5.7%. Partly offsetting this, 16 of the 25 projections agree that daily precipitation intensity will increase, which accounts for a model average 5.3% increase in annual precipitation. Between these conflicting tendencies, 12 projections show drier annual conditions by the 2060s and 13 show wetter. These results are obtained from 16 global general circulation models downscaled with different combinations of dynamical methods [Weather Research and Forecasting (WRF), Regional Spectral Model (RSM), and version 3 of the Regional Climate Model (RegCM3)] and statistical methods [bias correction with spatial disaggregation (BCSD) and bias correction with constructed analogs (BCCA)], although not all downscaling methods were applied to each global model. Model disagreements in the projected change in occurrence of the heaviest precipitation days (.60 mm day 21) account for the majority of disagreement in the projected change in annual precipitation, and occur preferentially over the Sierra Nevada and Northern California. When such events are excluded, nearly twice as many projections show drier future conditions.

Ensembles of historical climate simulations and climate projections from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset were investigated to determine how model credibility affects apparent relative scenario likelihoods in regional risk assessments. Methods were developed and applied in a Northern California case study. An ensemble of 59 twentieth century climate simulations from 17 WCRP CMIP3 models was analyzed to evaluate relative model credibility associated with a 75-member projection ensemble from the same 17 models. Credibility was assessed based on how models realistically reproduced selected statistics of historical climate relevant to California climatology. Metrics of this credibility were used to derive relative model weights leading to weight-threshold culling of models contributing to the projection ensemble. Density functions were then estimated for two projected quantities (temperature and precipitation), with and without considering credibility-based ensemble reductions. An analysis for Northern California showed that, while some models seem more capable at recreating limited aspects twentieth century climate, the overall tendency is for comparable model performance when several credibility measures are combined. Use of these metrics to decide which models to include in density function development led to local adjustments to function shapes, but led to limited affect

[1] The old and useful paradigm used by water resource engineers, that hydrology in a given place is stationary, and hence it is sufficient to look into the past to plan for the future, does not hold anymore, according to climate change projections. This becomes especially true in snow‐dominated regions like California, where not only the magnitude but also the timing of streamflow could be affected by changes in precipitation and temperature. To plan and operate water resources systems at the basin scale, it is necessary to develop new tools that are suited for this nonstationary world. In this paper we develop an optimization algorithm that can be used for different studies related to climate change and water resources management. Three applications of this algorithm are developed for the Merced River basin. The first of these gives an assessment of the climate change effects on the operations of this basin considering an adaptive management strategy embedded in the optimization algorithm. In a second application we explore different long‐term adaptation strategies intended to mitigate the effects of climate change. A final application is developed to determine how beneficial it is to build a new reservoir considering explicitly the uncertainty about future climate projections.