摘要 :
Wet-surface evaporation equations related to the Penman equation can be represented graphically on vapor pressure (e) versus temperature (T) graphs (Qualls & Crago, 2020, ). Here, actual regional evaporation is represented graphically on (e, T) graphs using the Complementary Relationship (CR) between actual and apparent potential evaporation. The CR proposed by the authors can be represented in a simple and intuitive geometric form, in which lines representing the regional latent heat flux, LE, and the wet surface (Priestley & Taylor, 1972, ) evaporation rate, LEPT, intersect at e = 0. This approach allows a graphical estimate of LE (or the corresponding mathematical formulation), provided available energy, wind speed, air temperature and humidity, and roughness lengths for momentum and sensible heat are available. The wet surface temperature is needed, and a calculation method for it is provided. The formulation works well using monthly data from seven sites in Australia, even when the same value of the Priestley & Taylor parameter alpha is used for all sites. Overall, compared to eddy covariance measurements, root mean square difference averaged 19 W m(-2); this compares favorably with the CR formulation proposed by Brutsaert (2...
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Wet-surface evaporation equations related to the Penman equation can be represented graphically on vapor pressure (e) versus temperature (T) graphs (Qualls & Crago, 2020, ). Here, actual regional evaporation is represented graphically on (e, T) graphs using the Complementary Relationship (CR) between actual and apparent potential evaporation. The CR proposed by the authors can be represented in a simple and intuitive geometric form, in which lines representing the regional latent heat flux, LE, and the wet surface (Priestley & Taylor, 1972, ) evaporation rate, LEPT, intersect at e = 0. This approach allows a graphical estimate of LE (or the corresponding mathematical formulation), provided available energy, wind speed, air temperature and humidity, and roughness lengths for momentum and sensible heat are available. The wet surface temperature is needed, and a calculation method for it is provided. The formulation works well using monthly data from seven sites in Australia, even when the same value of the Priestley & Taylor parameter alpha is used for all sites. Overall, compared to eddy covariance measurements, root mean square difference averaged 19 W m(-2); this compares favorably with the CR formulation proposed by Brutsaert (2015, ).
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Snow acts as a vital source of water especially in areas where streamflow relies on snowmelt. The spatiotemporal pattern of snow cover has tremendous value for snowmelt modeling. Instantaneous snow extent can be observed by remote...
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Snow acts as a vital source of water especially in areas where streamflow relies on snowmelt. The spatiotemporal pattern of snow cover has tremendous value for snowmelt modeling. Instantaneous snow extent can be observed by remote sensing. Cloud cover often interferes. Many complex methods exist to resolve this but often have requirements which delay the availability of the data and prohibit its use for real-time modeling. In this research, we propose a new method for spatially modeling snow cover throughout the melting season. The method ingests multiple years of MODerate Resolution Imaging Spectroradiometer snow cover data and combines it using principal component analysis to produce a spatial melt pattern model. Development and application of this model relies on the interannual recurrence of the seasonal melting pattern. This recurrence has long been accepted as fact but to our knowledge has not been utilized in remote sensing of snow. We develop and test the model in a large watershed in Wyoming using 17 years of remotely sensed snow cover images. When applied to images from 2 years that were not used in its development, the model represents snow-covered area with accuracy of 84.9-97.5% at varied snow-covered areas. The model also effectively removes cloud cover if any portion of the interface between land and snow is visible in a cloudy image. This new principal component analysis method for modeling the interannually recurring spatial melt pattern exclusively from remotely sensed images possesses its own intrinsic merit, in addition to those associated with its applications.Plain Language Summary Mountain snow provides an important source of water. The ability to model snowmelt and the resulting streamflow helps predict the amount and timing of when water will be available for irrigation, drinking water, and other uses. Satellite remote sensing can produce maps of snow-covered area that can improve our ability to model snowmelt, but during the melt season, clouds often block the view of watersheds from space. However, snowmelt follows a repeatable spatial pattern as it melts, year after year. We used this observation to develop a model of the spatial pattern of melt using multiple years of satellite snow cover images. This model can remove the cloud interference from daily satellite snow cover images. Our model achieved 85-98% accuracy in representing the spatial pattern of snow observed in satellite images even when starting with 95% cloud cover. Other models achieve similar accuracy but require much more data to accomplish this, which prevents them from being used for cloud removal in real time. We expect the model to improve our ability to model streamflow from snowmelt runoff.
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A rigorous approach to the complementary relationship (CR) in land surface evaporation was introduced by Brutsaert (2015, https://doi .org/10.1002/2015WR017720) in which the problem was cast in nondimensional form for generality, ...
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A rigorous approach to the complementary relationship (CR) in land surface evaporation was introduced by Brutsaert (2015, https://doi .org/10.1002/2015WR017720) in which the problem was cast in nondimensional form for generality, boundary conditions (BCs) were established from physical constraints, and a suitable mathematical solution was formulated. Building on Brutsaert's insightful foundation, Crago et al. (2016, https://doi .org/10.1002/2016WR019753) showed the need, for rational reasons, to modify Brutsaert's BCs by introducing E-max, an upper limit to the apparent potential evaporation corresponding to an actual evaporation of O. Following the rigorous approach of Brutsaert, this paper presents the derivation and resulting CR. The BC associated with E-max, requires a solution that is rescaled with regard to Brutsaert's dimensionless framework and represents a fundamentally different solution from that of Brutsaert. In essence, this formulation acknowledges a pattern of organized variability, which exists within the data when scaled in Brutsaert's dimensionless framework, and our rescaled CR reorganizes the data, collapsing it toward a more universal representation. Our rescaled CR, implemented with two versions of E-max, one based on mass transfer and another on the Penman equation, is evaluated alongside Brutsaert's original formulation. Multiyear data sets from seven Fluxnet sites in Australia, ranging from a sparsely vegetated ephemeral tropical wetland to a temperate forest with a 75-m-tall canopy were used to test the formulations on a weekly basis. All three formulations performed adequately. Overall, the rescaled model with E max based on the Penman equation performed best; it extracts more information with no additional observational data requirements.
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Potential (wet surface) evaporation is the basis for many methods to estimate actual evaporation. Penman's (1948, ) combination of energy budget with mass and energy transfer equations can be depicted on temperature-vapor pressure...
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Potential (wet surface) evaporation is the basis for many methods to estimate actual evaporation. Penman's (1948, ) combination of energy budget with mass and energy transfer equations can be depicted on temperature-vapor pressure graphs. Key to this depiction is two straight lines on the graph representing constant enthalpy of the air at measurement height and at the surface skin, with the gap between the lines representing a combination of aerodynamic resistance variables and available energy. The equations on which Penman based his formula are easily solved numerically (without need for Penman's famous assumption) for T-0w, the temperature the surface would have if it was saturated, keeping all other variables constant. Wet surface evaporation is proportional to the vapor pressure difference between the measurement height and the surface skin. Equilibrium evaporation, based on the slope of the vapor pressure curve at T-0w, is also easily represented in the graph. The difference between the correct wet surface evaporation rate and Penman's approximation is immediately visible on the graphs. The different wet surface evaporation rates are compared using data from a tropical savanna in Australia. Implications for the classic two-component interpretation of Penman's equation are discussed.
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The original and revised versions of the generalized complementary relationship (GCR) of evaporation (ET) were tested with six-digit Hydrologic Unit Code (HUC6) level long-term (1981-2010) water balance data (sample size of 334). ...
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The original and revised versions of the generalized complementary relationship (GCR) of evaporation (ET) were tested with six-digit Hydrologic Unit Code (HUC6) level long-term (1981-2010) water balance data (sample size of 334). The two versions of the GCR were calibrated with Parameter Elevation Regressions on Independent Slopes Model (PRISM) mean annual precipitation (P) data and validated against water-balance ET (ETwb) as the difference of mean annual HUC6-averaged P and United States Geological Survey HUC6 runoff (Q) rates. The original GCR overestimates P in about 18% of the PRISM grid points covering the contiguous United States in contrast with 12% of the revised version. With HUC6-averaged data the original version has a bias of -25 mm yr(-1) vs the revised version's -17 mm yr(-1), and it tends to more significantly underestimate ETwb at high values than the revised one (slope of the best fit line is 0.78 vs 0.91). At the same time it slightly outperforms the revised version in terms of the linear correlation coefficient (0.94 vs 0.93) and the root-mean-square error (90 vs 92 mm yr(-1)). (C) 2016 Elsevier B.V. All rights reserved.
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