Calculation of solar radiation in mountainous terrain

Flint, Alan L.; Childs, S. W.

doi:10.1016/0168-1923(87)90061-x

Cited by 76 publications

(57 citation statements)

References 7 publications

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“…Nevertheless, forcing uncertainty merits more attention in some cases, such as in snow-affected watersheds where meteorological and energy balance measurements are scarce (Bales et al, 2006;Schmucki et al, 2014) and prone to errors due to environmental or instrumental factors (Huwald et al, 2009;Lundquist et al, 2015;Rasmussen et al, 2012). Forcing uncertainty is enhanced in complex terrain where meteorological variables exhibit high spatial variability Flint and Childs, 1987;Herrero and Polo, 2012;Lundquist and Cayan, 2007). As a result, the choice of forcing data can yield substantial differences in calibrated model parameters and in modeled hydrologic processes, such as snowmelt and evapotranspiration Wayand et al, 2013).…”

Section: S Raleigh Et Al: Physical Model Sensitivity To Forcing mentioning

confidence: 99%

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

Raleigh

Lundquist

Clark

2015

Hydrol. Earth Syst. Sci.

162

128

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Abstract. Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snowaffected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

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Section: S Raleigh Et Al: Physical Model Sensitivity To Forcing mentioning

confidence: 99%

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

Raleigh

Lundquist

Clark

2015

Hydrol. Earth Syst. Sci.

162

128

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“…The scaling factors signify the range of values associated with each landscape attribute and the multipliers indicate the comparative importance. GIS data sets used in this analysis were derived from a variety of sources and include (i) soil erodibility index (kfactor; USDA-NRCS, 1994); (ii) Tahoe National Forest road network including road type; (iii) bedrock geology (1:250,000 scale); (iv) vegetation type (Davis and Stoms, 1996) with associated vegetation cover percentages estimated using field data; (v) slope and elevation data derived from a 30-m digital elevation model (DEM); (vi) potential evapotranspiration (PET) for the month of April calculated using a solar radiation model that relies on the DEM (Flint and Childs, 1987); (vii) digitized historic placer mine locations (Yeend, 1974); and (viii) mass wasting sites that were mapped and digitized as part of this study. GIS coverages were converted to 30-m grids, and a calculation of hillslope erosion potential was developed that accounts for all contributing factors.…”

Section: Gis Analysesmentioning

confidence: 99%

Conceptual model of sediment processes in the upper Yuba River watershed, Sierra Nevada, CA

et al. 2005

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This study examines the development of a conceptual model of sediment processes in the upper Yuba River watershed; and we hypothesize how components of the conceptual model may be spatially distributed using a geographical information system (GIS). The conceptual model illustrates key processes controlling sediment dynamics in the upper Yuba River watershed and was tested and revised using field measurements, aerial photography, and low elevation videography. Field reconnaissance included mass wasting and channel storage inventories, assessment of annual channel change in upland tributaries, and evaluation of the relative importance of sediment sources and transport processes. Hillslope erosion rates throughout the study area are relatively low when compared to more rapidly eroding landscapes such as the Pacific Northwest and notable hillslope sediment sources include highly erodible andesitic mudflows, serpentinized ultramafics, and unvegetated hydraulic mine pits. Mass wasting dominates surface erosion on the hillslopes; however, erosion of stored channel sediment is the primary contributor to annual sediment yield. We used GIS to spatially distribute the components of the conceptual model and created hillslope erosion potential and channel storage models. The GIS models exemplify the conceptual model in that landscapes with low potential evapotranspiration, sparse vegetation, steep slopes, erodible geology and soils, and high road densities display the greatest hillslope erosion potential and channel storage increases with increasing stream order. In-channel storage in upland tributaries impacted by hydraulic mining is an exception. Reworking of stored hydraulic mining sediment in low-order tributaries continues to elevate upper Yuba River sediment yields. Finally, we propose that spatially distributing the components of a conceptual model in a GIS framework provides a guide for developing more detailed sediment budgets or numerical models making it an inexpensive way to develop a roadmap for understanding sediment dynamics at a watershed scale. Published by Elsevier B.V.

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“…In the SGPGSA, southeast, south, and southwest aspects are the most prevalent. In addition to slope and aspect, shading and reflection of sunlight from surrounding terrain can have a big effect on calculated PET (Flint and Childs, 1987). To account for the shading effect of surrounding terrain, model input includes 36 blocking-ridge angles that were calculated at each 10-degree azimuth direction by using methods described in Hevesi and others (2003).…”

Section: Topographymentioning

confidence: 99%

“…2). PET is simulated by using an hourly time step to better represent the shading effects of rugged terrain relative to changes in solar position throughout the year (Flint and Childs, 1987). By using a modified form of the Priestley-Taylor equation (equations 10-14, U.S. Geological Survey, 2008), daily ET is simulated as a combined function of daily PET, the vertical distribution of available water in the root-zone layers, and the root-zone density, where the root-zone density represents the characteristics of vegetation.…”

Section: Root-zone Water Balancementioning

confidence: 99%

Estimating natural recharge in San Gorgonio Pass watersheds, California, 1913–2012

Hevesi¹,

Christensen²

2015

Scientific Investigations Report

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Calculation of solar radiation in mountainous terrain

Cited by 76 publications

References 7 publications

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

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

Conceptual model of sediment processes in the upper Yuba River watershed, Sierra Nevada, CA

Estimating natural recharge in San Gorgonio Pass watersheds, California, 1913–2012

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