Karl E. Lapo

Impact of errors in the downwelling irradiances on simulations of snow water equivalent, snow surface temperature, and the snow energy balance

The forcing irradiances (downwelling shortwave and longwave irradiances) are the primary drivers of snowmelt; however, in complex terrain, few observations, the use of estimated irradiances, and the influence of topography and elevation all lead to uncertainties in these radiative fluxes. The impact of uncertainties in the forcing irradiances on simulations of snow is evaluated in idealized modeling experiments. Two snow models of contrasting complexity, the Utah Energy Balance Model (UEB) and the Snow Thermal Model (SNTHERM), are forced with irradiances with prescribed errors of the structure and magnitude representative of those found in methods for estimating the downwelling irradiances. Relatively modest biases have substantial impacts on simulated snow water equivalent (SWE) and surface temperature (Ts) across a range of climates, whereas random noise at the daily scale has a negligible effect on modeled SWE and Ts. Shortwave biases have a smaller SWE impact, due to the influence of albedo, and Ts impact, due to their diurnal cycle, compared to equivalent longwave biases. Warmer sites exhibit greater sensitivity to errors when evaluated using SWE, while colder sites exhibit more sensitivity as evaluated using Ts. The two models displayed different sensitivity and responses to biases. The stability feedback in the turbulent fluxes explains differences in Ts between models in the negative longwave bias scenarios. When the models diverge during melt events, differences in the turbulent fluxes and internal energy change of the snow are found to be responsible. From this analysis, we suggest model evaluations use Ts in addition to SWE.

How Does Availability of Meteorological Forcing Data Impact Physically Based Snowpack Simulations

AbstractPhysically based models facilitate understanding of seasonal snow processes but require meteorological forcing data beyond air temperature and precipitation (e.g., wind, humidity, shortwave radiation, and longwave radiation) that are typically unavailable at automatic weather stations (AWSs) and instead are often represented with empirical estimates. Research is needed to understand which forcings (after temperature and precipitation) would most benefit snow modeling through expanded observation or improved estimation techniques. Here, the impact of forcing data availability on snow model output is assessed with data-withholding experiments using 3-yr datasets at well-instrumented sites in four climates. The interplay between forcing availability and model complexity is examined among the Utah Energy Balance (UEB), the Distributed Hydrology Soil Vegetation Model (DHSVM) snow submodel, and the snow thermal model (SNTHERM). Sixty-four unique forcing scenarios were evaluated, with different assumptio...

Using CERES SYN Surface Irradiance Data as Forcing for Snowmelt Simulation in Complex Terrain

AbstractThe benefit of using solar and longwave surface irradiance data from NASA’s Clouds and the Earth’s Radiant Energy System (CERES) synoptic (SYN) satellite product in simulations of snowmelt has been examined. The accuracy of the SYN downwelling solar and longwave irradiances was first assessed by comparison to measurements at NOAA’s Surface Radiation Network (SURFRAD) reference stations and to remote mountain observations. Typical shortwave (longwave) biases had magnitudes less than 30 (10) W m−2, with most standard deviations below 140 (30) W m−2. The performance of a range of snow models of varying complexity when using SYN irradiances as forcing data was then evaluated. Simulated snow water equivalent and runoff from cases using SYN data fell in the range of those from simulations forced with irradiances from well-maintained surface observation sites as well as empirical methods that have been shown to perform well in mountainous terrain. The SYN irradiances are therefore judged to be suitable f...

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.

A simple algorithm for identifying periods of snow accumulation on a radiometer

Downwelling solar, Qsi, and longwave, Qli, irradiances at the earths surface are the primary energy inputs for many hydrologic processes, and uncertainties in measurements of these two terms confound evaluations of estimated irradiances and negatively impact hydrologic modeling. Observations of Qsi and Qli in cold environments are subject to conditions that create additional uncertainties not encountered in other climates, specifically the accumulation of snow on uplooking radiometers. To address this issue, we present an automated method for estimating these periods of snow accumulation. Our method is based on forest interception of snow and uses common meteorological observations. In this algorithm, snow accumulation must exceed a threshold to obscure the sensor and is only removed through scouring by wind or melting. The algorithm is evaluated at two sites representing different mountain climates: (1) Snoqualmie Pass, Washington (maritime) and (2) the Senator Beck Basin Study Area, Colorado (continental). The algorithm agrees well with time-lapse camera observations at the Washington site and with multiple measurements at the Colorado site, with 70–80% of observed snow accumulation events correctly identified. We suggest using the method for quality controlling irradiance observations in snow-dominated climates where regular, daily maintenance is not possible.

A critical evaluation of modeled solar irradiance over California for hydrologic and land surface modeling

Studies of land surface processes in complex terrain often require estimates of meteorological variables, i.e., the incoming solar irradiance (Qsi), to force land surface models. However, estimates of Qsi are rarely evaluated within mountainous environments. We evaluated four methods of estimating Qsi: the CERES Synoptic Radiative Fluxes and Clouds (SYN) product, MTCLIM, a regional reanalysis product derived from a long-term Weather Research and Forecast simulation, and Mountain Microclimate Simulation Model (MTCLIM). These products are evaluated over the Central Valley and Sierra Nevada mountains in California, a region with meteorology strongly impacted by complex topography. We used a spatially dense network of Qsi observations (n = 70) to characterize the spatial characteristics of Qsi uncertainty. Observation sites were grouped into five subregions, and Qsi estimates were evaluated against observations in each subregion. Large monthly biases (up to 80 W m−2) outside the observational uncertainty were found for all estimates in all subregions examined, typically reaching a maximum in the spring. We found that MTCLIM and SYN generally perform the best across all subregions. Differences between Qsi estimates were largest over the Sierra Nevada, with seasonal differences exceeding 50 W m−2. Disagreements in Qsi were especially pronounced when averaging over high-elevation basins, with monthly differences up to 80 W m−2. Biases in estimated Qsi predominantly occurred with darker than normal conditions associated with precipitation (a proxy for cloud cover), while the presence of aerosols and water vapor was unable to explain the biases. Users of Qsi estimates in regions of complex topography, especially those estimating Qsi to force land surface models, need to be aware of this source of uncertainty.