Mountain system monitoring at Senator Beck Basin, San Juan Mountains, Colorado: A new integrative data source to develop and evaluate models of snow and hydrologic processes

Abstract

A hydrologic modeling data set is presented for water years 2006 through 2012 from the Senator Beck Basin (SBB) study area. SBB is a high altitude, 291 ha catchment in southwest Colorado exhibiting a continental, radiation-driven, alpine snow climate. Elevations range from 3362 m at the SBB pour point to 4118 m. Two study plots provide hourly forcing data including precipitation, wind speed, air temperature and humidity, global solar radiation, downwelling thermal radiation, and pressure. Validation data include snow depth, reflected solar radiation, snow surface infrared temperature, soil moisture, temperatures and heat flux, and stream discharge. Snow water equivalence and other snowpack properties are captured in snowpack profiles. An example of snow cover model testing using SBB data is discussed. Serially complete data sets are published including both measured data as well as alternative, corrected data and, in conjunction with validation data, expand the physiographic scope of published mountain system hydrologic data sets in support of advancements in snow hydrology modeling and understanding.

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

“…While previous investigations supported this idea for shortwave and longwave forcings in physically based snow models (i.e., Lapo et al, 2015), the current study showed that biases are more important than random errors for all commonly required meteorological forcings (and not just irradiances). The model was more sensitive to biases and less sensitive to random errors due to the systematic nature of biases.…”

“…However, these studies have typically been limited to (1) empirical/conceptual models (He et al, 2011a, b;Raleigh and Lundquist, 2012;Shamir and Georgakakos, 2006;Slater and Clark, 2006), (2) errors for a subset of forcings (e.g., precipitation or temperature only) (Burles and Boon, 2011;Dadic et al, 2013;Durand and Margulis, 2008;Lapo et al, 2015;Xia et al, 2005), (3) model sensitivity to choice of forcing parameterization (e.g., longwave) without considering uncertainty in parameterization inputs (e.g., temperature and humidity) (Guan et al, 2013), and (4) simple representations of forcing errors (e.g., Kavetski et al, 2006a, b). The last is evident in studies that only consider single types of forcing errors (e.g., bias) and single distributions (e.g., uniform) and examines errors separately (Burles and Boon, 2011;Koivusalo and Heikinheimo, 1999;Raleigh and Lundquist, 2012;Xia et al, 2005).…”

“…The last is evident in studies that only consider single types of forcing errors (e.g., bias) and single distributions (e.g., uniform) and examines errors separately (Burles and Boon, 2011;Koivusalo and Heikinheimo, 1999;Raleigh and Lundquist, 2012;Xia et al, 2005). Lapo et al (2015) show that biases have a greater impact than random errors on modeled snow water equivalent and surface temperature but their analysis only considers longwave and shortwave forcings and considers errors separately. Examining uncertainty in one factor at a time remains popular but fails to explore the uncertainty space adequately, ignoring potential interactions between forcing errors Saltelli, 1999).…”

“…The sites included (1) the Imnavait Creek (IC, 930 m) site (Euskirchen et al, 2012;Kane et al, 1991;Sturm and Wagner, 2010), located in the tundra north of the Brooks Range in Alaska, USA, (2) the maritime Col de Porte (CDP, 1330 m) site (Morin et al, 2012) in the Chartreuse Range in the Rhône-Alpes of France, (3) the intermountain Reynolds Mountain East (RME, 2060 m) sheltered site (Reba et al, 2011) in the Owyhee Range in Idaho, USA, and (4) the continental Swamp Angel Study Plot (SASP, 3370 m) site (Landry et al, 2014) in the San Juan Mountains of Colorado, USA. We selected these sites because of the quality and completeness of the forcing data and because they spanned contrasting climates (Table 1), allowing us to check for potential climate dependencies in sensitivity to forcing errors.…”

Improving the representation of hydrologic processes in Earth System Models

“…The research data must be compiled in a consistent format, be serially complete (at least for the model forcing data), and include estimates of the uncertainty and spatial representativeness of individual observations [Abramowitz, 2012]. Many of the emerging data papers from research basins cover these issues to varying degrees [e.g., Reba et al, 2011;Landry et al, 2014], but there is much more work to be done to pull together research data from a wide range of watersheds.…”