Brian Henn

High-Elevation Precipitation Patterns: Using Snow Measurements to Assess Daily Gridded Datasets across the Sierra Nevada, California*

AbstractGridded spatiotemporal maps of precipitation are essential for hydrometeorological and ecological analyses. In the United States, most of these datasets are developed using the Cooperative Observer (COOP) network of ground-based precipitation measurements, interpolation, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) to map these measurements to places where data are not available. Here, we evaluate two daily datasets gridded at ° resolution against independent daily observations from over 100 snow pillows in California’s Sierra Nevada from 1990 to 2010. Over the entire period, the gridded datasets performed reasonably well, with median total water-year errors generally falling within ±10%. However, errors in individual storm events sometimes exceeded 50% for the median difference across all stations, and in many cases, the same underpredicted storms appear in both datasets. Synoptic analysis reveals that these underpredicted storms coincide with 700-hPa winds from the...

A Comparison of Methods for Filling Gaps in Hourly Near-Surface Air Temperature Data

AbstractNear-surface air temperature observations often have periods of missing data, and many applications using these datasets require filling in all missing periods. Multiple methods are available to fill missing data, but the comparative accuracy of these approaches has not been assessed. In this comparative study, five techniques were used to fill in missing temperature data: spatiotemporal correlations in the form of empirical orthogonal functions (EOFs), time series diurnal interpolation, and three variations of lapse rate–based filling. The method validation used sets of hourly surface temperature observations in complex terrain from five regions. The most accurate method for filling missing data depended on the number of available stations and the number of hours of missing data. Spatiotemporal correlations using EOF reconstruction were most accurate provided that at least 16 stations were available. Temporal interpolation was the most accurate method when only one or two stations were available ...

Estimating mountain basin-mean precipitation from streamflow using Bayesian inference

Estimating basin-mean precipitation in complex terrain is difficult due to uncertainty in the topographical representativeness of precipitation gauges relative to the basin. To address this issue, we use Bayesian methodology coupled with a multi-model framework to infer basin-mean precipitation from streamflow observations, and we apply this approach to snow-dominated basins in the Sierra Nevada of California. Using streamflow observations, forcing data from lower-elevations stations, the Bayesian Total Error Analysis (BATEA) methodology and the Framework for Understanding Structural Errors (FUSE), we infer basin-mean precipitation, and compare it to basin-mean precipitation estimated using topographically-informed interpolation from gauges (PRISM, the Parameter-elevation Regression on Independent Slopes Model). The BATEA-inferred spatial patterns of precipitation show agreement with PRISM in terms of the rank of basins from wet to dry, but differ in absolute values. In some of the basins, these differences may reflect biases in PRISM, because some implied PRISM runoff ratios may be inconsistent with the regional climate. We also infer annual time series of basin precipitation using a two-step calibration approach. Assessment of the precision and robustness of the BATEA approach suggests that uncertainty in the BATEA-inferred precipitation is primarily related to uncertainties in hydrologic model structure. Despite these limitations, time series of inferred annual precipitation under different model and parameter assumptions are strongly correlated with one another, suggesting that this approach is capable of resolving year-to-year variability in basin-mean precipitation. This article is protected by copyright. All rights reserved.

Yosemite Hydroclimate Network: Distributed stream and atmospheric data for the Tuolumne River watershed and surroundings

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 dataset of distributed stream stage, streamflow, stream temperature, barometric pressure, and air temperature from the Tuolumne River Watershed in Yosemite National Park, Sierra Nevada, California, U.S.A. for water years 2002 to 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 to 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 datasets collected by cooperating agencies, referenced herein. This dataset 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. This article is protected by copyright. All rights reserved.

Combining snow, streamflow, and precipitation gauge observations to infer basin‐mean precipitation

Precipitation data in mountain basins is typically sparse and subject to uncertainty due to difficulties in measurement and capturing spatial variability. Streamflow provides indirect information about basin-mean precipitation, but inferring precipitation from streamflow requires assumptions about hydrologic model structure that influence precipitation amounts. In this study, we test the extent to which using both snow and streamflow observations reduces differences in inferred annual total precipitation, compared to inference from streamflow alone. The case study area is the upper Tuolumne River basin in the Sierra Nevada of California, where distributed and basin-mean snow water equivalent (SWE) estimates have been made using LiDAR as part of the NASA Airborne Snow Observatory (ASO). To reconstruct basin-mean SWE for years prior to the ASO campaign, we test for a robust relationship between SWE estimates from ASO and from snow courses and pillows, which have a longer record. Relative to ASOs distributed SWE observations, point SWE measurements in this part of the Sierra Nevada tend to overestimate SWE at a given elevation, but under-sample high-elevation areas. We then infer precipitation from snow and streamflow, obtained from multiple hydrologic model structures. When included in precipitation inference, snow data reduce by up to one third the standard deviations of the water year total precipitation between model structures, and improve the consistency between structures in terms of the yearly variability in precipitation. We reiterate previous findings that multiple types of hydrologic data improve the consistency of modeled physical processes and help identify the most appropriate model structures. This article is protected by copyright. All rights reserved.

Hydroclimatic Conditions Preceding the March 2014 Oso Landslide

The 22 March 2014 Oso landslide was one of the deadliest in U.S. history, resulting in 43 fatalities and the destructionofmorethan40structures.Weexaminesynopticconditions,precipitationrecords,andsoilmoisture reconstructionsinthedays,months,andyearsprecedingthelandslide.Atmosphericreanalysisshowsaperiodof

High‐Elevation Evapotranspiration Estimates During Drought: Using Streamflow and NASA Airborne Snow Observatory SWE Observations to Close the Upper Tuolumne River Basin Water Balance

Hydrologic variables such as evapotranspiration (ET) and soil water storage are difficult to observe across spatial scales in complex terrain. Streamflow and lidar-derived snow observations provide information about distributed hydrologic processes such as snowmelt, infiltration, and storage. We use a distributed streamflow data set across eight basins in the upper Tuolumne River region of Yosemite National Park in the Sierra Nevada mountain range, and the NASA Airborne Snow Observatory (ASO) lidarderived snow data set over 3 years (2013–2015) during a prolonged drought in California, to estimate basinscale water balance components. We compare snowmelt and cumulative precipitation over periods from the ASO flight to the end of the water year against cumulative streamflow observations. The basin water balance residual term (snow melt plus precipitation minus streamflow) is calculated for each basin and year. Using soil moisture observations and hydrologic model simulations, we show that the residual term represents short-term changes in basin water storage over the snowmelt season, but that over the period from peak snow water equivalent (SWE) to the end of summer, it represents cumulative basin-mean ET. Warmseason ET estimated from this approach is 168 (85–252 at 95% confidence), 162 (0–326) and 191 (48–334) mm averaged across the basins in 2013, 2014, and 2015, respectively. These values are lower than previous full-year and point ET estimates in the Sierra Nevada, potentially reflecting reduced ET during drought, the effects of spatial variability, and the part-year time period. Using streamflow and ASO snow observations, we quantify spatially-distributed hydrologic processes otherwise difficult to observe. Plain Language Summary The amount of evapotranspiration in the Sierra Nevada mountains is important because this water is not available for downstream uses, supports alpine ecosystems, and may change in a future climate. Currently there are few measurements of evapotranspiration in the Sierra Nevada across a diverse landscape. We use a high-resolution snow data set (NASA’s Airborne Snow Observatory) with multiple stream gauge observations from Yosemite National Park to estimate evapotranspiration using a water balance approach. Over 2013–2015 during the California drought, we find that evapotranspiration averages 162–191 mm per year, over the time period from peak snowpack in the spring to the end of the summer. Compared with other estimates of evapotranspiration, we find that the estimates are smaller, perhaps due to the diverse spatial terrain sampled by this approach. We also find that the estimates vary only slightly from year to year during the California drought. Our study may help understand how evapotranspiration, and thus available water supply, may change in a warmer future climate.