Publications

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2019
Roche, JW, Rice R, Meng XD, Cayan DR, Dettinger MD, Alden D, Patel SC, Mason MA, Conklin MH, Bales RC.  2019.  Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA. Earth System Science Data. 11:101-110.   10.5194/essd-11-101-2019   AbstractWebsite

sWe present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010-2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2-6) from four locations spanning the rain-snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories (https://doi.org/10.6071/M3FH3D).

2018
Sumargo, E, Cayan DR.  2018.  The influence of cloudiness on hydrologic fluctuations in the mountains of the western United States. Water Resources Research. 54:8478-8499.   10.1029/2018wr022687   AbstractWebsite

This study investigates snowmelt and streamflow responses to cloudiness variability across the mountainous parts of the western United States. Twenty years (1996-2015) of Geostationary Operational Environmental Satellite-derived cloud cover indices (CC) with 4-km spatial and daily temporal resolutions are used as a proxy for cloudiness. The primary driver of nonseasonal fluctuations in daily mean solar insolation is the fluctuating cloudiness. We find that CC fluctuations are related to snowmelt and snow-fed streamflow fluctuations, to some extent (correlations of <0.5). Multivariate linear regression models of daily snowmelt (MELT) and streamflow (AQ) variations are constructed for each month from February to July, when snowmelt is most active. Predictors include CC from five antecedent days up to the current day. The CC-MELT and CC-AQ associations vary with time and location. The results show the dominance of negative correlations between CC and MELT, exemplifying the cloud-shading (or clear-sky) effect on snowmelt. The magnitude of the CC-MELT association (R-2) amounts to 5-61%, typically peaking in May. These associations fade earlier in summer during dry years than wet years, indicating the differing responses of thicker versus thinner snowpack. The CC-AQ association displays a less consistent pattern, with R-2 amounting to 2-47%. Nevertheless, MELT and AQ fluctuations exhibit spatially extensive patterns of correlations with daily cloudiness anomalies, indicating that the effects of cloudiness often operate over regional spatial scales. Plain Language Summary Much of the water supply in the western United States originates as mountain streams, which derive much of their water from snowmelt. The primary driver of mountain snowmelt is solar energy, and cloud cover regulates how much solar energy can reach the snow surface. Despite this fact, how snowmelt and streamflow respond to cloud cover (or its absence) has not been thoroughly studied. In our study, we describe snowmelt and streamflow responses to cloud cover using satellite images of cloud cover and surface records of snowmelt and streamflow. We find significant snowmelt and daily streamflow rate responses to cloud cover. Importantly, during the peak snowmelt season, snowmelt and streamflow decrease when cloud cover increases, and vice versa, confirming the cloud-shading effect on the snow surface. However, this cause-and-effect process is not so simple. We also find that cloud cover (or its absence) in the previous few days can affect how much snow melts and the streamflow rate is in a day. Snowmelt and streamflow responses to cloud cover are stronger, albeit shorter-lived, in dry years than in wet years, highlighting the relative importance of cloud cover in drier years.

Knowles, N, Cronkite-Ratcliff C, Pierce DW, Cayan DR.  2018.  Responses of unimpaired flows, storage, and managed flows to scenarios of climate change in the San Francisco Bay-Delta Watershed. Water Resources Research. 54:7631-7650.   10.1029/2018wr022852   AbstractWebsite

Projections of meteorology downscaled from global climate model runs were used to drive a model of unimpaired hydrology of the Sacramento/San Joaquin watershed, which in turn drove models of operational responses and managed flows. Twenty daily climate change scenarios for water years 1980-2099 were evaluated with the goal of producing inflow boundary conditions for a watershed sediment model and for a hydrodynamical model of the San Francisco Bay-Delta estuary. The resulting time series of meteorology, snowpack, unimpaired flow, reservoir storage, and managed flow were analyzed for century-scale trends. In the Sacramento basin, which dominates Bay-Delta inflows, all 20 scenarios portrayed warming trends (with a mean of 4.1 degrees C) and most had precipitation increases (with a mean increase of 9%). Sacramento basin snowpack water equivalent declined sharply (by 89%), which was associated with a major shift toward earlier unimpaired runoff timing (33% more flow arriving prior to 1 April). Sacramento basin reservoirs showed large declines in end-of-September storage. Water-year averaged outflows increased for most scenarios for both unimpaired and impaired flows, and frequency of extremely high daily unimpaired and impaired flows increased (increases of 175% and 170%, respectively). Managed Delta inflows were projected to experience large increases in the wet season and declines in the dry season. Changes in management strategy and infrastructure can mitigate some of these changes, though to what degree is uncertain.

2016
Lundquist, JD, Roche JW, Forrester H, Moore C, Keenan E, Perry G, Cristea N, Henn B, Lapo K, McGurk B, Cayan DR, Dettinger MD.  2016.  Yosemite Hydroclimate Network: Distributed stream and atmospheric data for the Tuolumne River watershed and surroundings. Water Resources Research. 52:7478-7489.   10.1002/2016wr019261   AbstractWebsite

Regions of complex topography and remote wilderness terrain have spatially varying patterns of temperature and streamflow, but due to inherent difficulties of access, are often very poorly sampled. Here we present a data set of distributed stream stage, streamflow, stream temperature, barometric pressure, and air temperature from the Tuolumne River Watershed in Yosemite National Park, Sierra Nevada, California, USA, for water years 2002-2015, as well as a quality-controlled hourly meteorological forcing time series for use in hydrologic modeling. We also provide snow data and daily inflow to the Hetch Hetchy Reservoir for 1970-2015. This paper describes data collected using low-visibility and low-impact installations for wilderness locations and can be used alone or as a critical supplement to ancillary data sets collected by cooperating agencies, referenced herein. This data set provides a unique opportunity to understand spatial patterns and scaling of hydroclimatic processes in complex terrain and can be used to evaluate downscaling techniques or distributed modeling. The paper also provides an example methodology and lessons learned in conducting hydroclimatic monitoring in remote wilderness.

2013
Pierce, DW, Das T, Cayan DR, Maurer EP, Miller NL, Bao Y, Kanamitsu M, Yoshimura K, Snyder MA, Sloan LC, Franco G, Tyree M.  2013.  Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling. Climate Dynamics. 40:839-856.   10.1007/s00382-012-1337-9   AbstractWebsite

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.

Pierce, DW, Cayan DR, Das T, Maurer EP, Miller NL, Bao Y, Kanamitsu M, Yoshimura K, Snyder MA, Sloan LC, Franco G, Tyree M.  2013.  The key role of heavy precipitation events in climate model disagreements of future annual precipitation changes in California. Journal of Climate. 26:5879-5896.   10.1175/jcli-d-12-00766.1   AbstractWebsite

Climate model simulations disagree on whether future precipitation will increase or decrease over California, 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(-1)) 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(-1). This reduces California'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(-1)) 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.

2011
Das, T, Pierce DW, Cayan DR, Vano JA, Lettenmaier DP.  2011.  The importance of warm season warming to western US streamflow changes. Geophysical Research Letters. 38   10.1029/2011gl049660   AbstractWebsite

Warm season climate warming will be a key driver of annual streamflow changes in four major river basins of the western U.S., as shown by hydrological model simulations using fixed precipitation and idealized seasonal temperature changes based on climate projections with SRES A2 forcing. Warm season (April-September) warming reduces streamflow throughout the year; streamflow declines both immediately and in the subsequent cool season. Cool season (October-March) warming, by contrast, increases streamflow immediately, partially compensating for streamflow reductions during the subsequent warm season. A uniform warm season warming of 3 C drives a wide range of annual flow declines across the basins: 13.3%, 7.2%, 1.8%, and 3.6% in the Colorado, Columbia, Northern and Southern Sierra basins, respectively. The same warming applied during the cool season gives annual declines of only 3.5%, 1.7%, 2.1%, and 3.1%, respectively. Citation: Das, T., D. W. Pierce, D. R. Cayan, J. A. Vano, and D. P. Lettenmaier (2011), The importance of warm season warming to western U. S. streamflow changes, Geophys. Res. Lett., 38, L23403, doi: 10.1029/2011GL049660.

2010
Maurer, EP, Hidalgo HG, Das T, Dettinger MD, Cayan DR.  2010.  The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California. Hydrology and Earth System Sciences. 14:1125-1138.   10.5194/hess-14-1125-2010   AbstractWebsite

Three statistical downscaling methods were applied to NCEP/NCAR reanalysis (used as a surrogate for the best possible general circulation model), and the downscaled meteorology was used to drive a hydrologic model over California. The historic record was divided into an 'observed' period of 1950-1976 to provide the basis for downscaling, and a 'projected' period of 1977-1999 for assessing skill. The downscaling methods included a bias-correction/spatial downscaling method (BCSD), which relies solely on monthly large scale meteorology and resamples the historical record to obtain daily sequences, a constructed analogues approach (CA), which uses daily large-scale anomalies, and a hybrid method (BCCA) using a quantile-mapping bias correction on the large-scale data prior to the CA approach. At 11 sites we compared three simulated daily flow statistics: streamflow timing, 3-day peak flow, and 7-day low flow. While all downscaling methods produced reasonable streamflow statistics at most locations, the BCCA method consistently outperformed the other methods, capturing the daily large-scale skill and translating it to simulated streamflows that more skillfully reproduced observationally-driven streamflows.

2005
Stewart, IT, Cayan DR, Dettinger MD.  2005.  Changes toward earlier streamflow timing across western North America. Journal of Climate. 18:1136-1155.   10.1175/jcli3321.1   AbstractWebsite

The highly variable timing of streamflow in snowmelt-dominated basins across western North America is an important consequence, and indicator, of climate fluctuations. Changes in the timing of snowmelt-derived streamflow from 1948 to 2002 were investigated in a network of 302 western North America gauges by examining the center of mass for flow, spring pulse onset dates, and seasonal fractional flows through trend and principal component analyses. Statistical analysis of the streamflow timing measures with Pacific climate indicators identified local and key large-scale processes that govern the regionally coherent parts of the changes and their relative importance. Widespread and regionally coherent trends toward earlier onsets of springtime snowmelt and streamflow have taken place across most of western North America, affecting an area that is much larger than previously recognized. These timing changes have resulted in increasing fractions of annual flow occurring earlier in the water year by 1-4 weeks. The immediate (or proximal) forcings for the spatially coherent parts of the year-to-year fluctuations and longer-term trends of streamflow timing have been higher winter and spring temperatures. Although these temperature changes are partly controlled by the decadal-scale Pacific climate mode [Pacific decadal oscillation (PDO)], a separate ani significant part of the variance is associated with a springtime warming trend that spans the PDO phases.