Lapse Rates and Spatial Interpolation of Air Temperature in Mountainous Terrain
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Lapse rate
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Abstract In distributed hydrological modeling, surface air temperature ( T air ) is of great importance in simulating cold region processes, while the near‐surface‐air‐temperature lapse rate (NLR) is crucial to prepare T air (when interpolating T air from site observations to model grids). In this study, a distributed biosphere hydrological model with improved snow physics (WEB‐DHM‐S) was rigorously evaluated in a typical cold, large river basin (e.g., the upper Yellow River basin), given a mean monthly NLRs. Based on the validated model, we have examined the influence of the NLR on the simulated snow processes and streamflows. We found that the NLR has a large effect on the simulated streamflows, with a maximum difference of greater than 24% among the various scenarios for NLRs considered. To supplement the insufficient number of monitoring sites for near‐surface‐air‐temperature at developing/undeveloped mountain regions, the nighttime Moderate Resolution Imaging Spectroradiometer land surface temperature is used as an alternative to derive the approximate NLR at a finer spatial scale (e.g., at different elevation bands, different land covers, different aspects, and different snow conditions). Using satellite‐based estimation of NLR, the modeling of snow processes has been greatly refined. Results show that both the determination of rainfall/snowfall and the snowpack process were significantly improved, contributing to a reduced summer evapotranspiration and thus an improved streamflow simulation.
Snowpack
Lapse rate
Moderate-resolution imaging spectroradiometer
Snowmelt
Elevation (ballistics)
Shortwave radiation
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A spatially distributed model for the simulation of snow accumulation and melt is presented. Watershed information on topography, vegetation and soils in digital terrain models (overlays) serve as the data base for watershed analysis, classification of snow in Landsat imagery and automatic generation of parameter decks for operating distributed simulation models of snowcover dynamics and streamflow generation. Snow processes are simulated within variable size grid-cell elements. The hydrograph resulting from spring snowmelt is simulated by a lateral flow model of streamflow generation driven by simulated snowmelt and rain inputs. Options are avilable for simulating the effects of forest management alternatives on selected areas. Snow course measurements and classified Landsat imagery are used for updating simulated parameters.
Snowmelt
Distributed element model
Simulation Modeling
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Snowmelt
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Shortwave radiation
Snowmelt
Shortwave
Shading
Albedo (alchemy)
Interpolation
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The snowmelt runoff model (SRM) is used to simulate streamflow from snowmelt in the Er'dao-Songhua basin of upper Songhuajiang basin from March to August in 2010. The basin is divided into three elevation zones based on SRTM DEM data. MODIS flexible snow cover products (MODISMC) are generated form daily Terra/Aqua products and used as snow cover area input for SRM model. The precipitation and temperature data from climate station are interpolated by Kriging methods to calculate daily average precipitation and temperature for each zone. SRM model is forced by three variables and eight parameters considering the physical and hydrological feature of study area. Results show that the peak of snowmelt runoff comes in the middle of April and the end of May. The Nash-Sutcliffe coefficient of determination (R 2 ) and deviation of the runoff volumes (D v ) is 0.57 and 25.59% respectively. The model errors are mainly caused by ignoring the physical process of snowmelt and lacking enough in-situ materials.
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Abstract Hydrological processes in mountainous settings depend on snow distribution, whose prediction accuracy is a function of model spatial scale. Although model accuracy is expected to improve with finer spatial resolution, an increase in resolution comes with modelling costs related to increased computational time and greater input data and parameter information. This computational and data collection expense is still a limiting factor for many large watersheds. Thus, this work's main objective is to question which physical processes lead to loss in model accuracy with regard to input spatial resolution under different climatic conditions and elevation ranges. To address this objective, a spatially distributed snow model, iSnobal , was run with inputs distributed at 50‐m—our benchmark for comparison—and 100‐m resolutions and with aggregated (averaged from the fine to the large resolution) inputs from the 50‐m model to 100‐, 250‐, 500‐, and 750‐m resolution for wet, average, and dry years over the Upper Boise River Basin (6,963 km 2 ), which spans four elevation bands: rain dominated, rain–snow transition, and snow dominated below treeline and above treeline. Residuals, defined as differences between values quantified with high resolution (>50 m) models minus the benchmark model (50 m), of simulated snow‐covered area (SCA) and snow water equivalent (SWE) were generally slight in the aggregated scenarios. This was due to transferring the effects of topography on meteorological variables from the 50‐m model to the coarser scales through aggregation. Residuals in SCA and SWE in the distributed 100‐m simulation were greater than those of the aggregated 750 m. Topographic features such as slope and aspect were simplified, and their gradient was reduced due to coarsening the topography from the 50‐ to 100‐m resolution. Therefore, solar radiation was overestimated, and snow drifting was modified and caused substantial SCA and SWE underestimation in the distributed 100‐m model relative to the 50‐m model. Large residuals were observed in the wet year and at the highest elevation band when and where snow mass was large. These results support that model accuracy is substantially reduced with model scales coarser than 50 m.
Elevation (ballistics)
Temporal resolution
Hydrological modelling
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Snow line
Forest cover
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This work sentnow a method of periodic evaluation of snow-covered areas by digital processing of Landsat data. Since the s cover was mapped for the first time in a large and morphologicomplex alpine basin, it was necessary to develop procedures to determine the snow coverage for partly clouded regions or for incomplete satellite scenes. The changing areal extent of the seasonal snow cover is an important variable for deterministic snowmelt runoff models. By using the SRM model, the natural runoff in the Rhein-Felsberg basin (3249 kM, 571-3614 m a.s.I) was simulated although the measured river flows are significantly influenced by artificial reservoir operation. Such simmulation would not be possible by calibration models that optimize the model parameters by the measured discharge.
Snowmelt
Water year
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