Areal Depletion Research Articles

Tuning the quantum oscillations of surface Dirac electrons in the topological insulator Bi2Te2Se by liquid gating

In Bi${}_{2}$Te${}_{2}$Se, the period of quantum oscillations arising from surface Dirac fermions can be increased sixfold using ionic liquid gating. At high gate voltages, the Fermi energy reaches the $N=1$ Landau level in a 14-T field. This enables the $\frac{1}{2}$ shift predicted for the Dirac spectrum to be measured accurately. A surprising result is that liquid gating strongly enhances the surface mobility. By analyzing the Hall conductivity, we show that the enhancement occurs on only one surface. We present evidence that the gating process is fully reversible (hence consistent with band bending by the $E$ field from the anion layer accumulated). In addition to the surface carriers, the experiment yields the mobility and density of the bulk carriers in the impurity band. By analyzing the charge accumulation vs gate voltage, we also obtain estimates of the depletion width and the areal depletion capacitance ${C}_{d}/A$. The value of ${C}_{d}/A$ implies an enhanced electronic polarizability in the depletion region.

Calibration of a distributed snow model using MODIS snow covered area data

Spatial ground-based observations of snow are often limited at the watershed-scale, therefore the snow modeling component of a hydrologic modeling system is often calibrated along with the rainfall–runoff model using watershed discharge observations. This practice works relatively well for lumped modeling applications when the accuracy of sub-watershed processes is generally not of concern. However, with the increasing use of distributed models, realistic representation of processes, such as snow areal depletion, become more important. In this study, we test the use of snow covered area (SCA) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite for calibration of four key parameters in the distributed US National Weather Service (NWS) SNOW17 model in the North Fork of the American River basin in California, USA. Three tests are conducted; two rely solely on MODIS SCA data and one includes discharge in the calibration procedure. The three calibrations are compared to the use of parameters obtained from the NWS California Nevada River Forecast Center (CNRFC). The calibration approach that utilizes both MODIS SCA and discharge data produces the most accurate spatial (gridded) SCA and basin discharge simulations but not the best SCA summary statistics. In general it was found that improvement in simulated SCA when averaged and evaluated by elevation zone using standard summary statistics, does not necessarily coincide with more accurate discharge simulations.

Distributed assimilation of satellite‐based snow extent for improving simulated streamflow in mountainous, dense forests: An example over the DMIP2 western basins

Snow cover area affects snowmelt, soil moisture, evapotranspiration, and ultimately streamflow. For the Distributed Model Intercomparison Project – Phase 2 Western basins, we assimilate satellite‐based fractional snow cover area (fSCA) from the Moderate Resolution Imaging Spectroradiometer, or MODIS, into the National Weather Service (NWS) SNOW‐17 model. This model is coupled with the NWS Sacramento Heat Transfer (SAC‐HT) model inside the National Aeronautics and Space Administration's (NASA) Land Information System. SNOW‐17 computes fSCA from snow water equivalent (SWE) values using an areal depletion curve. Using a direct insertion, we assimilate fSCAs in two fully distributed ways: (1) we update the curve by attempting SWE preservation, and (2) we reconstruct SWEs using the curve. The preceding are refinements of an existing simple, conceptually guided NWS algorithm. Satellite fSCA over dense forests inadequately accounts for below‐canopy snow, degrading simulated streamflow upon assimilation during snowmelt. Accordingly, we implement a below‐canopy allowance during assimilation. This simplistic allowance and direct insertion are found to be inadequate for improving calibrated results, still degrading them as mentioned above. However, for streamflow volume for the uncalibrated runs, we obtain: (1) substantial to major improvements (64–81%) as a percentage of the control run residuals (or distance from observations), and (2) minor improvements (16–22%) as a percentage of observed values. We highlight the need for detailed representations of canopy‐snow optical radiative transfer processes in mountainous, dense forest regions if assimilation‐based improvements are to be seen in calibrated runs over these areas.

Noah LSM Snow Model Diagnostics and Enhancements

Abstract A negative snow water equivalent (SWE) bias in the snow model of the Noah land surface scheme used in the NCEP suite of numerical weather and climate prediction models has been noted by several investigators. This bias motivated a series of offline tests of model extensions and improvements intended to reduce or eliminate the bias. These improvements consist of changes to the model’s albedo formulation that include a parameterization for snowpack aging, changes to how pack temperature is computed, and inclusion of a provision for refreeze of liquid water in the pack. Less extensive testing was done on the performance of model extensions with alternate areal depletion parameterizations. Model improvements were evaluated through comparisons of point simulations with National Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) SWE data for deep-mountain snowpacks at selected stations in the western United States, as well as simulations of snow areal extent over the conterminous United States (CONUS) domain, compared with observational data from the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS). The combination of snow-albedo decay and liquid-water refreeze results in substantial improvements in the magnitude and timing of peak SWE, as well as increased snow-covered extent at large scales. Modifications to areal snow depletion thresholds yielded more realistic snow-covered albedos at large scales.

An approach to using snow areal depletion curves inferred from MODIS and its application to land surface modelling in Alaska

Snowcover areal depletion curves inferred from the moderate resolution imaging spectroradiometer (MODIS) are validated and then applied in NASA’s catchment-based land surface model (CLSM) for numerical simulations of hydrometeorological processes in the Kuparuk River basin (KRB) of Alaska. The results demonstrate that the MODIS snowcover fraction f derived from a simple relationship in terms of the normalized difference snow index compares well with Landsat values over the range 20 � f � 100%. For f< 20%, however, MODIS 500 m subpixel data underestimate the amount of snow by up to 13% compared with Landsat at spatial resolutions of 30 m binned to equivalent 500 m pixels. After a bias correction, MODIS snow areal depletion curves during the spring transition period of 2002 for the KRB exhibit similar features to those derived from surface-based observations. These results are applied in the CLSM subgrid-scale snow parameterization that includes a deep and a shallow snowcover fraction. Simulations of the evolution of the snowpack and of freshwater discharge rates for the KRB over a period of 11 years are then analysed with the inclusion of this feature. It is shown that persistent snowdrifts on the arctic landscape, associated with a secondary plateau in the snow areal depletion curves, are hydrologically important. An automated method is developed to generate the shallow and deep snowcover fractions from MODIS snow areal depletion curves. This provides the means to apply the CLSM subgrid-scale snow parameterization in all watersheds subject to seasonal snowcovers. Improved simulations and predictions of the global surface energy and water budgets are expected with the incorporation of the MODIS snow data into the CLSM. Copyright  2005 John Wiley & Sons, Ltd.

Validation of VEGETATION, MODIS, and GOES + SSM/I snow‐cover products over Canada based on surface snow depth observations

AbstractThe ability to map the areal depletion of snow accurately is important for operational decision making (e.g. reservoir management), for correct specification of boundary conditions in numerical weather‐prediction models, and for modelling atmospheric, hydrological and ecological processes. A number of satellite‐derived snow‐cover products are available in real time; however, these can differ considerably due to variations in sensor and platform characteristics, data pre‐processing methods, and the particular snow‐cover classification algorithms employed. This article evaluates the performance of three daily snow‐cover products over Canada: (1) Terra Moderate Resolution Imaging Spectroradiometer (MODIS) snow‐cover maps provided at 500 m spatial resolution for 2001; (2) National Oceanic Atmospheric Administration (NOAA) GOES + SSM/I snow maps provided at 4 km resolution for 2001 (∼30 km resolution SSM/I data were used for cloud‐covered areas); (3) SPOT‐4 VEGETATION (VGT) snow maps derived at 1 km resolution for 2000. An evaluation of the snow‐cover products with daily surface snow depth observations collected from almost 2000 meteorological stations across Canada revealed that the VGT snow product used in this study may not be suitable for snow mapping in Canada because of a significant bias towards mapping snow‐free conditions. The MODIS and NOAA products showed similar reasonable levels of agreement with ground data, ranging from approximately 80% to 100% on a monthly basis. Somewhat lower agreement was found in January, when solar zenith angles are large, suggesting that better correction for tree and surface shadow effects is needed in current snow‐cover mapping algorithms. The lowest agreement was seen during snowmelt, mainly in forest areas. Comparison of MODIS agreement statistics between sparse and dense conifer regions indicated that the effect of non‐representativenes of surface snow depth observations was on the order of 10% disagreement. The NOAA product was found to be the most consistent among land cover types and had the highest percentage of cloud‐free pixels. Copyright © 2004 John Wiley &amp; Sons, Ltd.

A Land Cover‐Based Snow Cover Representation for Distributed Hydrologic Models

A snow cover depletion curve (SDC) summarizes the relationship between snow cover distribution and an average snow cover property, such as depth or water equivalent, for a given area. Snow cover depletion curves have been developed for, and applied in, hydrological models on a watershed or elevation zone basis. However, land cover‐;based SDCs are not prominent in the literature. For this study the areal distribution of snow cover for dominant land cover units was measured during the winters of 1991 and 1992 in the Laurel Creek watershed in southern Ontario, Canada. On the basis of these data a general model for land cover‐;based SDCs is developed for these land cover units, namely, short grass, ploughed fields, and deciduous forests. This model is derived from the three‐parameter lognormal distribution, which is shown to characterize the areal depletion curves of the land cover units studied. The SDCs based on this new model provide a formal distributed snow cover representation that can be used in vegetation‐based distributed hydrological models requiring accurate spatial representations of snow cover attributes.

Modelling the areal depletion of snowcover in a forested catchment

Successful modelling of snowmelt runoff from drainage basins often requires information concerning changes in the snowpack area during the course of the melt. Such data can be provided through the use of feedback models which employ the snowpack's areal extent and observed or estimated melts to forecast the snow-covered area for the subsequent day. The structure of five of these models is examined along with the results of a field study examining snowmelt and snowpack depletion in a forested catchment in south-central Ontario. The predictions of snowcover depletion for each model are compared with the observed trends for three distinct melt environments. The results suggest that several of these models can be used successfully to simulate changes in snowcover extent during snowmelt in forested areas. Snowpack depletion in areas of discontinuous snowcover is best simulated by models that assume that melt occurs predominantly at the snowpack margins, while models that utilize observed spatial variations in peak snowpack water-equivalent and assume either uniform or spatially variable melt depths perform best in environments with continuous snowcover prior to melt.

Quantification of the Lake Trophic Typologies of Naumann (Surface Quality) and Thienemann (Oxygen) with Special Reference to the Great Lakes

Separate trophic scales and indices are developed for two of the most significant symptoms of eutrophication: surface water quality and hypolimnetic dissolved oxygen depletion. The scales are made comparable by expressing them in dimensionless form with a lower bound of zero and a mesotrophic range from 5 to 10. In this way, the two symptoms can be compared and their relative importance judged. This is done for the Great Lakes with the result that for both scales Lakes Superior, Huron, and Michigan are classified as oligotrophic. However, while central and eastern Lake Erie and Lake Ontario are classified as mesotrophic in terms of surface water quality, they range from eutrophic (central Lake Erie) to oligotrophic (Lake Ontario) on the oxygen scale. This is because, although these lakes are similar in surface water quality, their hypolimnion thicknesses range from approximately 4 m for central Erie to 70 m for Lake Ontario. Because of its shallowness, western Lake Erie does not have a persistent oxygen problem. In terms of surface quality it is classified as eutrophic. We have attempted to relate the two scales by correlating surface primary production and areal depletion rate. The results indicate that for lakes of similar primary production, areal oxygen depletion is directly proportional to hypolimnion thickness.